# Susceptibility Assessment of Flash Floods: A Bibliometrics Analysis and Review

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{7}

^{8}

^{9}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Research Methods

#### 2.2. Data Sources

## 3. Results

#### 3.1. Analysis of the Number of Publications and Citation Frequency

- (1)
- The first stage: The period from 2007 to 2015 is the embryonic stage of flash flood susceptibility assessment, in which the number of articles published in the research field every year was less than 10 per year, the number of citations per article per year was not much, and an obvious structural interruption could be seen in 2008 and 2011.
- (2)
- The second stage: The period from 2016 to 2019 is the exploration stage of the development of this field, in which 26 articles were published per year. Although only 18 articles were published in 2018, the number of citations (448) was about twice that of 2017 (the number of articles published was 22), indicating that the field of flash flood susceptibility assessment received significant attention in 2018. This result was consistent with that of Franceschinis [77]. In addition, Franceschinis et al. [77] conducted a questionnaire on risk awareness of residents in 2005 and 2018. In 2005, residents did not have knowledge and experience of floods and believed that the possibility of occurrence of floods in the future was extremely low. Still, in 2018, the number of residents decreased significantly, and frequent floods increased people’s attention and research efforts.
- (3)
- The third stage: Since 2020, the number of articles published and the number of citations per article per year have increased significantly. The number of articles published in 2020 reached 67, and the number of citations topped 1442, reaching a new height. This stage is a stage of vigorous development in this field. The research work from 2007 to 2015 accounted for 11.5% of all research work. The relative share rose to 34.10% from 2016 to 2019 and reached 54.4% after 2020, indicating that flash flood susceptibility assessment will be the research hotspot in the following years.

#### 3.2. Influence Analysis

#### 3.2.1. Analysis of Authors’ Productivity

_{max})/2, where n

_{max}represents the maximum number of papers published by authors in this field, and authors who have published more than M papers in this field are considered as core authors. Through the analysis of the core collection database of WOS, it was found that Costache is the author who has published the most significant number of papers (22 articles in total, n

_{max}= 22). The M value is 8.24, so the author who has published more than eight articles is the core author, as shown in Figure 3.

#### 3.2.2. Analysis of the Influence of Countries/Regions

#### 3.2.3. Influence of Publications and Journals

#### 3.2.4. Analysis of the Influence of Funding Institutions

#### 3.3. Keyword Analysis

#### 3.3.1. Keyword Co-Occurrence Analysis

#### 3.3.2. Keyword Burst Analysis

- (1)
- The first stage is the period from 2010 to 2015. At this stage, the keyword “debris flow” had a maximum strength of 3.6, followed by landslide and flood, showing that scholars who studied the flash flood susceptibility assessment during this stage mainly discussed the hazard assessment of debris flows and landslides. The word “debris flow” originally covered flash floods, so the combined events of debris flows and flash floods were studied in many articles [114,115]. For example, Stolle et al. [116] analyzed the susceptibility of debris flow in the Ladakh Range by morphometric method, but later researchers found that the identification of different disaster processes by debris flow would affect the analysis of risk assessment and others [117], so more and more independent studies had been conducted for flash floods after 2015. In addition, as rainfall was the main disaster-causing factor, Mei et al. [118] conducted their research on precipitation estimation in the river basin in the eastern Italian Alps, which was also the main research area at this stage.
- (2)
- The second stage is the period from 2015 to 2017. The keywords with the highest strength were “susceptibility” (5.55) and “social vulnerability” (4.7). Social vulnerability assessment could understand the impact of flash floods and was an important part of flood risk assessment framework [119]. Remote sensing was one of the key words in this period. The map of the research area and the data of the influencing factors could be obtained by remote sensing technology, which provided sufficient preparation for the research.
- (3)
- The third stage is the period from 2018 to 2022. The keywords of “decision tree” had the maximum strength (with strength of 2.8), followed by the keyword of “rotation tree” and “discriminant analysis” (both of which were 2.1). At this stage, the research on the flash flood susceptibility assessment focused on the potential index and assessment model of flash floods. The accurate determination of the assessment index of flash flood susceptibility could help improve the model’s accuracy. The weight was an important determination criterion, so the strength of the importance was larger at this stage. With the introduction of artificial intelligence and the exposure of models such as “rotation forest”, “decision forest”, and “morphometric analysis”, the assessment models of flash flood susceptibility became more diversified.
- (4)
- The research and analysis of the above three development periods found that the academic community explored more and more deeply the flash flood susceptibility assessment and tended to determine high-precision assessment models and complete susceptibility assessment.

#### 3.4. Author Co-Citation Analysis

#### 3.5. Institutional Co-Operation Analysis

## 4. Main Subfields of Flash Flood Susceptibility Assessment

#### 4.1. Assessment Scale

#### 4.2. Assessment Unit

#### 4.2.1. Type of the Assessment Unit

^{2}to divide Ushairy Basin into 17 sub-basins; Mahmood et al. [75] adopted a threshold of 25 km

^{2}to divide Panjkora Basin into 30 sub-basins; El-Fakharany et al. [133] divided the Heliopolis basin into 10 sub-basins; Singh et al. [99] divided Ganga Basin into 29 sub-basins. However, not all basins were divided into sub-basin units for assessment, and many researchers used grid units for assessment [47,134].

#### 4.2.2. Size of the Assessment Unit

#### 4.3. Assessment Index

_{1}and X

_{2}(and more X factors) interact with the dependent variable Y. The factor detector can judge the correlation between independent variables and dependent variables. The advantage of this method is that the results are intuitive and reliable, and no assumptions or restrictions on independent and dependent variables are required.

#### 4.4. Assessment Model

- (1)
- Intelligent learning model: This model selects appropriate flash flood susceptibility assessment indexes based on artificial intelligence and big data, conducts data test, determines the sample set and realizes the training dataset through a specific algorithm. The models such as artificial neural network (ANN), deep learning neural network (DLNN), random forest model, multilayer perceptron (MLP), particle swarm optimization (PSO), support vector machine (SVM), etc. are often used in the research of flash flood susceptibility.
- (2)
- Statistical model: This model is generated by applied mathematical and statistical methods, which generally analyze the relationship between independent and dependent variables by determining the weight or probability of each independent variable. Frequency ratio (FR), statistical index (SI) model, logistic regression, consequences of evidence model (WOE), fuzzy analytical hierarchy process (FAHP), and analytical hierarchy process (AHP), etc., are often used in the study of flash flood susceptibility.
- (3)
- Hydrological model: This model simulates the occurrence process of flash floods with factors, such as rainfall, hydrological soil, land cover, etc., quantifies surface runoff, and obtains flash flood sensitivity map with the help of remote sensing and geographic information system technology for further susceptibility assessment.
- (4)
- Hydrodynamic model: This model determines the boundary conditions and simulates the possibility of flash flood disasters by using hydrodynamics equations (such as Manning Formula, Energy Equation, Saint-Venant Equation, etc.).
- (5)
- Hybrid model: The model is an integration of a GIS-based intelligent learning model, statistical model, hydrological model, and hydrodynamic model, including a multi-intelligent learning model, dual statistical model, intelligent learning-statistical model, intelligent learning-hydrodynamic model, and hydrological-hydrodynamic model. Most of the studies adopt multi-intelligent learning models, dual statistical models, and intelligent learning-statistical models, while very few scholars use intelligent learning-hydrodynamic models and hydrological-hydrodynamic models for their studies.

#### 4.5. Accuracy Assessment

- (1)
- ROC-AUC method

- (2)
- Error statistical index

^{2}), etc. Generally, the statistical index combined with more than three indexes is used for evaluation, and different indexes may generate models with additional optimal accuracy. The frequently used error statistical indexes in the study are listed in Table 11.

## 5. Application of Remote Sensing and GIS in Flash Flood Susceptibility Assessment

- (1)
- Application of remote sensing and GIS in the flash flood susceptibility assessment scale

- (2)
- Application of remote sensing and GIS in flash flood susceptibility assessment unit

- (3)
- Application of remote sensing and GIS in flash flood susceptibility assessment index

- (4)
- Application of remote sensing and GIS in a flash flood susceptibility assessment model

## 6. Discussions

## 7. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Haque, M.N.; Siddika, S.; Sresto, M.A.; Saroar, M.M.; Shabab, K.R. Geo-spatial Analysis for Flash Flood Susceptibility Mapping in the North-East Haor (Wetland) Region in Bangladesh. J. Earth Syst. Environ.
**2021**, 5, 365–384. [Google Scholar] [CrossRef] - Ahmad, W.; Nasir, M.J.; Iqbal, J. Flash flood susceptibility modeling for drainage basins of Dir Lower Khyber-Pakhtunkhwa: A comparative analysis of morphometric ranking and El-Shamy’s approach. In Proceedings of the SPIE Conference Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII, Online Only, UK, 21–25 September 2020; p. 115280E. [Google Scholar]
- Einfalt, T.; Hatzfeld, F.; Wagner, A.; Seltmann, J.; Castro, D.; Frerichs, S. Urbas: Forecasting and management of flash floods in urban areas. Urb. Water
**2009**, 6, 369–374. [Google Scholar] [CrossRef] - Ma, M.; He, B.; Wan, J.; Jia, P.; Guo, X.; Gao, L.; Maguire, L.W.; Hong, Y. Characterizing the Flash Flooding Risks from 2011 to 2016 over China. Water
**2018**, 10, 704. [Google Scholar] [CrossRef] [Green Version] - Zeng, Z.; Tang, G.; Long, D.; Xu, H.; Chen, Y.; Hong, Y. Development of GIS-based FFPI for China’s flash flood forecasting. In Proceedings of the 2015 23rd International Conference on Geoinformatics, Wuhan, China, 19–21 June 2015; p. 15702354. [Google Scholar]
- Dragomir, A.; Tudorache, A.V.; Costache, R. Assessement of flash-flood susceptibility in Small river basins. Present Environ. Sustain. Dev.
**2020**, 14, 119–130. [Google Scholar] - Xiong, J.; Pang, Q.; Fan, C.; Cheng, W.; Ye, C.; Zhao, Y.; He, Y.; Cao, Y. Spatiotemporal Characteristics and Driving Force Analysis of Flash Floods in Fujian Province. ISPRS Int. J. Geo-Inf.
**2020**, 9, 133. [Google Scholar] [CrossRef] [Green Version] - Li, H.; Zhang, S.; Li, Q.; Zhang, X.; Guo, L. Research on flash flood disaster warning index: Case study of Luoning County. J. IOP Conf. Ser. Earth Environ. Sci.
**2019**, 218, 012075. [Google Scholar] [CrossRef] - Wang, N.; Lombardo, L.; Tonini, M.; Cheng, W.; Guo, L.; Xiong, J. Spatiotemporal clustering of flash floods in a changing climate (China, 1950–2015). J. Nat. Hazards Earth Syst. Sci.
**2020**, 21, 2109–2124. [Google Scholar] [CrossRef] - Alrehaili, N.R. A systematic review of the emergency planning for flash floods response in the Kingdom of Saudi Arabia. Aust. Inst. Disaster Resil.
**2021**, 36, 82–88. [Google Scholar] [CrossRef] - Zhang, Y.; Wang, Y.; Chen, Y.; Xu, Y.; Zhang, G.; Lin, Q.; Luo, R. Projection of changes in flash flood occurrence under climate change at tourist attractions. J. Hydrol.
**2021**, 595, 126039. [Google Scholar] [CrossRef] - Lin, K.; Chen, H.; Xu, C.; Yan, P.; Lan, T.; Liu, Z.Y.; Dong, C.Y. Assessment of flash flood risk based on improved analytic hierarchy process method and integrated maximum likelihood clustering algorithm. J. Hydrol.
**2020**, 584, 124696. [Google Scholar] [CrossRef] - Younis, J.; Anquetin, S.; Thielen, J. The benefit of high-resolution operational weather forecasts for flash flood warning. Hydrol. Earth Syst. Sci.
**2018**, 12, 1039–1051. [Google Scholar] [CrossRef] - Goyal, H.R.; Ghanshala, K.K.; Sharma, S. Flash flood risk management modeling in indian cities using IoT based reinforcement learning. Mater. Today Proc.
**2021**, 46, 10533–10538. [Google Scholar] [CrossRef] - Arshad, S.H.M.; Ishak, A.M.H.; Muhamad, N.; Pereira, J.J. Assessing Effectiveness of Kuala Lumpur SMART Tunnel for Flood Prevention. Geogafia-Malays. J. Soc. Space
**2020**, 16, 184–200. [Google Scholar] - Hang, H.T.; Hoa, P.D.; Tru, V.N.; Phuong, N.V. Application of Shannon’s Entropy Model and GIS in Flash Flood Forecasting Along National Highway-6, Hoa Binh Province, Vietnam. Int. J. Geomate
**2021**, 21, 50–57. [Google Scholar] [CrossRef] - Li, H.C.; Lei, X.H.; Shang, Y.Z.; Qin, T. Flash flood early warning research in China. Int. J. Water Resour. Dev.
**2018**, 34, 369–385. [Google Scholar] [CrossRef] [Green Version] - Liu, Y.; Yuan, X.; Guo, L.; Huang, Y.; Zhang, X. Driving Force Analysis of the Temporal and Spatial Distribution of Flash Floods in Sichuan Province. Sustainability
**2017**, 9, 1527. [Google Scholar] [CrossRef] [Green Version] - Wang, H.; Yun, R.; Zhao, R.; Qi, Z. Flash flood risk evaluation based on variable fuzzy method and fuzzy clustering analysis. J. Intell. Fuzzy Syst.
**2019**, 37, 4861–4872. [Google Scholar] [CrossRef] - Gourley, J.J.; Flamig, Z.L.; Vergara, H.; Kirstetter, P.E.; Clark, R.A.; Argyle, E.; Arthur, A.; Martinaitis, S.; Terti, G.; Erlingis, J.M. The FLASH project: Improving the tools for flash flood monitoring and prediction across the United States. Bull. Am. Meteorol. Soc.
**2017**, 98, 361–372. [Google Scholar] [CrossRef] - Shahabi, H.; Shirzadi, A.; Ronoud, S.; Asadi, S.; Pham, B.T.; Mansouripour, F.; Geertsema, M.; Clague, J.J.; Bui, D.T. Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm. Geosci. Front.
**2021**, 12, 101100. [Google Scholar] [CrossRef] - Arlikatti, S.; Maghelal, P.; Agnimitra, N.; Chatterjee, V. Should I stay or should I go? Mitigation strategies for flash flooding in India. J. Int. J. Disaster Risk Reduct.
**2018**, 27, 48–56. [Google Scholar] [CrossRef] - Youssef, A.M.; Pradhan, B.; Sefry, S.A. Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environ. Earth Sci.
**2015**, 75, 12. [Google Scholar] [CrossRef] - Giuseppe, E.; Fabio, M.; Germana, S. Analysis of Increasing Flash Flood Frequency in the Densely Urbanized Coastline of the Campi Flegrei Volcanic Area, Italy. J. Front. Earth Sci.
**2018**, 6, 63. [Google Scholar] - Alam, A.; Ahmed, B.; Sammonds, P. Flash flood susceptibility assessment using the parameters of drainage basin morphometry in SE Bangladesh. Quat. Int.
**2022**, 575, 295–307. [Google Scholar] [CrossRef] - Arnell, N.W.; Gosling, S.N. The impacts of climate change on river flood risk at the global scale. J. Clim. Change
**2016**, 134, 387–401. [Google Scholar] [CrossRef] [Green Version] - Popa, M.C.; Simion, A.G.; Peptenatu, D.; Dima, C.; Draghici, C.C.; Florescu, M.S.; Dobrea, C.R.; Diaconu, D.C. Spatial assessment of flash-flood vulnerability in the Moldova river catchment (N Romania) using the FFPI. J. Flood Risk Manag.
**2020**, 13, e12624. [Google Scholar] [CrossRef] - Marchi, L.; Borga, M.; Preciso, E.; Gaume, E. Characterisation of selected extreme flash floods in Europe and implications for flood risk management. J. Hydrol.
**2010**, 394, 118–133. [Google Scholar] [CrossRef] - Zhai, X.; Zhang, Y.; Zhang, Y.; Guo, L.; Liu, R. Simulating flash flood hydrographs and behavior metrics across China: Implications for flash flood management. Sci. Total Environ.
**2021**, 763, 142977. [Google Scholar] [CrossRef] - Guo, L.; He, B.; Ma, M.; Chang, Q.; Li, Q.; Zhang, K.; Hong, Y. A comprehensive flash flood defense system in China: Overview, achievements, and outlook. J. Nat. Hazards
**2018**, 92, 727–740. [Google Scholar] [CrossRef] - Costache, R. Flash-Flood Potential Assessment in the Upper and Middle Sector of Prahova River Catchment (Romania). A Comparative Approach between Four Hybrid Models. Sci. Total Environ.
**2019**, 659, 1115–1134. [Google Scholar] [CrossRef] - Abuzied, S.; Yuan, M.; Ibrahim, S.; Kaiser, M.; Saleem, T. Geospatial risk assessment of flash floods in Nuweiba area, Egypt. J. Arid. Environ.
**2016**, 133, 54–72. [Google Scholar] [CrossRef] - Band, S.S.; Janizadeh, S.; Chandra Pal, S.; Saha, A.; Chakrabortty, R.; Melesse, A.M.; Mosavi, A. Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms. Remote Sens.
**2020**, 12, 3568. [Google Scholar] [CrossRef] - Ngo, P.; Pham, T.D.; Nhu, V.H.; Le, T.T.; Tran, D.A.; Phan, D.C.; Hoa, P.V.; Amaro-Mellado, J.L.; Bui, D.T. A novel hybrid quantum-PSO and credal decision tree ensemble for tropical cyclone induced flash flood susceptibility mapping with geospatial data. J. Hydrol.
**2020**, 596, 125682. [Google Scholar] [CrossRef] - Nasir, M.J.; Iqbal, J.; Ahmad, W. Flash flood risk modeling of swat river sub-watershed: A comparative analysis of morphometric ranking approach and El-Shamy approach. J. Arab. J. Geosci.
**2020**, 13, 1082. [Google Scholar] [CrossRef] - Pham, B.T.; Phong, T.V.; Nguyen, H.D.; Qi, C.; Al-Ansari, N.; Amini, A.; Ho, L.S.; Tuyen, T.T.; Yen, H.P.H.; Ly, H.B.; et al. A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping. Water
**2020**, 12, 239. [Google Scholar] [CrossRef] [Green Version] - Wu, Z.; Bhattacharya, B.; Xie, P.; Zevenbergen, C. Improving flash flood forecasting using a frequentist approach to identify rainfall thresholds for flash flood occurrence. Stoch. Environ. Res. Risk Assess.
**2022**. [Google Scholar] [CrossRef] - Clark, R.A.; Gourley, J.J.; Flamig, Z.L.; Hong, Y.; Clark, E. CONUS-wide evaluation of national weather service flash flood guidance products. Weather Forecast.
**2014**, 29, 377–392. [Google Scholar] [CrossRef] [Green Version] - Bournas, A.; Baltas, E. Investigation of the Gridded Flash Flood Guidance in a Peri-Urban Basin in Greater Athens area, Greece. J. Hydrol.
**2022**, 610, 127820. [Google Scholar] [CrossRef] - Adnan, M.S.G.; Dewan, A.; Zannat, K.E.; Abdullah, A.M. The use of watershed geomorphic data in flash flood susceptibility zoning: A case study of the Karnaphuli and Sangu river basins of Bangladesh. J. Nat. Hazards
**2019**, 99, 425–448. [Google Scholar] [CrossRef] - Ha, H.; Luu, C.; Bui, Q.D.; Pham, D.H.; Hoang, T.; Nguyen, V.P.; Vu, M.T.; Pham, B.T. Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models. Nat. Hazards
**2021**, 109, 1247–1270. [Google Scholar] [CrossRef] - Khosravi, K.; Pourghasemi, H.R.; Chapi, K.; Bahri, M. Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: A comparison between Shannon’s entropy, statistical index, and weighting factor models. Environ. Monit. Assess.
**2016**, 188, 656. [Google Scholar] [CrossRef] - Mahmood, S.; Rahman, A.U. Flash flood susceptibility modelling using geomorphometric approach in the Ushairy Basin, eastern Hindu Kush. J. Earth Syst. Sci.
**2019**, 128, 97. [Google Scholar] [CrossRef] [Green Version] - Cao, Y.; Jia, H.; Xiong, J.; Cheng, W.; Li, K.; Pang, Q.; Yong, Z. Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China. ISPRS Int. J. Geo-Inf.
**2020**, 9, 748. [Google Scholar] [CrossRef] - Pham, B.T.; Avand, M.; Janizadeh, S.; Phong, T.V.; Al-Ansari, N.; Ho, L.S.; Das, S.; Le, H.V.; Amini, A.; Bozchaloei, S.K.; et al. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. Water
**2020**, 12, 683. [Google Scholar] [CrossRef] - Saber, M.; Boulmaiz, T.; Guermoui, M.; Abdrado, K.I.; Kantoush, S.A.; Sumi, T.; Boutaghane, H.; Nohara, D.; Mabrouk, E. Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction. Geocarto Int.
**2021**. [Google Scholar] [CrossRef] - Costache, R.; Bui, D.T. Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles. Sci. Total Environ.
**2020**, 712, 136492. [Google Scholar] [CrossRef] - Costache, R.; Pham, Q.B.; Sharifi, E.; Linh, N.T.T.; Abba, S.I.; Vojtek, M.; Vojteková, J.; Nhi, P.T.T.; Khoi, D.N. Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques. Remote Sens.
**2020**, 12, 106. [Google Scholar] [CrossRef] [Green Version] - Janizadeh, S.; Avand, M.; Jaafari, A.; Phong, T.V.; Bayat, M.; Ahmadisharaf, E.; Prakash, I.; Pham, B.T.; Lee, S. Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability
**2019**, 11, 5426. [Google Scholar] [CrossRef] [Green Version] - Nlansour, R.; El Ghali, A. Digital mapping of the risk of flooding in the North-East of Tunisia by the frequency ratio method and the statistical index. Rev. Int. Geomat.
**2020**, 29, 339–360. [Google Scholar] - Darabi, H.; Haghighi, A.T.; Rahmati, O.; Shahrood, A.J.; Rouzbeh, S.; Pradhan, B.; Bui, D.T. A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation. J. Hydrol.
**2021**, 603, 126854. [Google Scholar] [CrossRef] - Costache, R.D.; Arabameri, A.; Moayedi, H.; Pham, Q.B.; Santosh, M.; Nguyen, H.; Pandey, M.; Pham, B.T. Flash-Flood Potential Index estimation using Fuzzy Logic combined with Deep Learning Neural Network, Nave Bayes, XGBoost and Classification and Regression Tree. J. Geocarto Int.
**2021**, 37, 6780–6807. [Google Scholar] [CrossRef] - Liu, C.; Guo, L.; Ye, L.; Zhang, S.; Zhao, Y.; Song, T. A review of advances in China’s flash flood early-warning system. Nat. Hazards
**2018**, 92, 619–634. [Google Scholar] [CrossRef] [Green Version] - Saleh, A.; Yuzir, A.; Abustan, I. Flash flood susceptibility modelling: A review. IOP Conf. Ser. Mater. Sci. Eng.
**2020**, 712, 012005. [Google Scholar] [CrossRef] - Ali, K.; Bajracharyar, R.M.; Raut, N. Advances and challenges in flash flood risk assessment: A review. J. Geogr. Nat. Disasters
**2017**, 7, 2. [Google Scholar] [CrossRef] [Green Version] - Salman, A.M.; Li, Y. Flood risk assessment, future trend modeling, and risk communication: A review of ongoing research. Nat. Hazards Rev.
**2018**, 19, 04018011. [Google Scholar] [CrossRef] - Hapuarachchi, H.A.P.; Wang, Q.J.; Pagano, T.C. A review of advances in flash flood forecasting. Hydrol. Process.
**2011**, 25, 2771–2784. [Google Scholar] [CrossRef] - De Moel, H.; Jongman, B.; Kreibich, H.; Merz, B.; Penning-Rowsell, E.; Ward, P.J. Flood risk assessments at different spatial scales. Mitig. Adapt. Strategy Glob. Change
**2015**, 20, 865–890. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Hakim, W.L.; Lee, C.-W. A review on remote sensing and GIS applications to monitor natural disasters in Indonesia. Korean J. Remote Sens.
**2020**, 36, 1303–1322. [Google Scholar] - Li, W.; Zhao, Y. Bibliometric analysis of global environmental assessment research in a 20-year period. Environ. Impact Assess. Rev.
**2015**, 50, 158–166. [Google Scholar] [CrossRef] - Wang, X.; Zhao, W.; Wang, Y.; Zhao, Q.; Yang, X.; Su, K.; Gu, D. Visual Analysis on Information Theory and Science of Complexity Approaches in Healthcare Research. Entropy
**2020**, 22, 109. [Google Scholar] [CrossRef] [Green Version] - Pritchard, J. Statistical-Bibliography or Bibliometrics? J. Doc.
**1969**, 25, 348–349. [Google Scholar] - Ni, Y.; Zhang, T.; Xu, L.; Han, P. Research on the Evolution Path of Sentiment Analysis Technology Based on Bibliometrics. In Proceedings of the 2021 International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA), Xi’an, China, 28–30 May 2021; pp. 150–157. [Google Scholar]
- Ye, M.; Yang, L.; Lu, J. Research on Information Management of Big Data Based on Bibliometrics. In Proceedings of the 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 11–13 October 2019; pp. 1465–1469. [Google Scholar]
- Huang, L.; Wang, X.; Wu, F.; Li, Q. Analysis on Evolution and Frontier Research of Selective Catalyst Reduction Technology for Diesel Engine Based on Bibliometrics. In Proceedings of the 2017 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR, USA, 9–13 July 2017; p. 17413554. [Google Scholar]
- Zuccala, A. Inciting the metric oriented humanist: Teaching bibliometrics in a faculty of humanities. J. Educ. Inf.
**2016**, 32, 149–164. [Google Scholar] [CrossRef] [Green Version] - Noyons, E.C.M.; Moed, H.F.; Luwel, M. Combining mapping and citation analysis for evaluative bibliometric purposes. J. Am. Soc. Inf. Sci.
**1999**, 50, 115–131. [Google Scholar] [CrossRef] - Herrera-Viedma, E.; Martinez, M.; Herrera, M. Bibliometric tools for discovering information in database. In Trends in Applied Knowledge-Based Systems and Data Science, Proceedings of the 29th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Morioka, Japan, 2–4 August 2016; Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M., Eds.; Springer: Cham, Switzerland, 2016; Volume 9799, pp. 193–203. [Google Scholar]
- Che, S.; Kamphuis, P.; Zhang, S.; Zhao, X.; Kim, J.H. A Visualization Analysis of Crisis and Risk Communication Research Using CiteSpace. Int. J. Environ. Res. Public Health
**2022**, 19, 2923. [Google Scholar] [CrossRef] [PubMed] - Chen, C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol.
**2006**, 57, 359–377. [Google Scholar] [CrossRef] [Green Version] - Chen, C. Science Mapping: A Systematic Review of the Literature. J. Data Inf. Sci.
**2017**, 2, 1–40. [Google Scholar] [CrossRef] [Green Version] - Cui, Y.; Mou, J.; Liu, Y. Knowledge mapping of social commerce research: A visual analysis using CiteSpace. Electron. Commer. Res.
**2018**, 18, 837–868. [Google Scholar] [CrossRef] - Srivastava, P.; Jovane, L. Misinterpreting proxy data for paleoclimate signals: A comment on Shukla et al. 2020. J. Holocene
**2020**, 30, 1866–1873. [Google Scholar] [CrossRef] - Costache, R.; Hong, H.; Pham, Q.B. Comparative Assessment of the Flash-Flood Potential within Small Mountain Catchments Using Bivariate Statistics and Their Novel Hybrid Integration with Machine Learning Models. Sci. Total Environ.
**2020**, 711, 134514. [Google Scholar] [CrossRef] - Mahmood, S.; Rahman, A. Flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan. Environ. Earth Sci.
**2019**, 78, 43. [Google Scholar] [CrossRef] - Bui, D.T.; Tsangaratos, P.; Ngo, P.T.T.; Pham, T.D.; Pham, B.T. Flash Flood Susceptibility Modeling Using an Optimized Fuzzy Rule Based Feature Selection Technique and Tree Based Ensemble Methods. Sci. Total Environ.
**2019**, 668, 1038–1054. [Google Scholar] [CrossRef] - Franceschinis, C.; Thiene, M.; Di Baldassarre, G.; Mondino, E.; Scolobig, A.; Borga, M. Heterogeneity in flood risk awareness: A longitudinal, latent class model approach. J. Hydrol.
**2021**, 599, 126255. [Google Scholar] [CrossRef] - Khosravi, K.; Pham, B.T.; Chapi, K.; Shirzadi, A.; Shahabi, H.; Revhaug, I.; Prakash, I.; Tien Bui, D.; Pham, B.T.; Chapi, K.; et al. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci. Total Environ.
**2018**, 627, 744–755. [Google Scholar] [CrossRef] [PubMed] - De Moel, H.; Aerts, J.C.J.H. Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates. Nat. Hazards
**2011**, 58, 407–425. [Google Scholar] [CrossRef] [Green Version] - Hong, H.; Panahi, M.; Shirzadi, A.; Ma, T.; Liu, J.; Zhu, A.X.; Chen, W.; Kougias, I.; Kazakis, N.; Panahi, M.; et al. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci. Total Environ.
**2018**, 621, 1124–1141. [Google Scholar] [CrossRef] [PubMed] - Rahmati, O.; Zeinivand, H.; Besharat, M. Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat. Nat. Hazards Risk
**2016**, 7, 1000–1017. [Google Scholar] [CrossRef] [Green Version] - Collier, C.G. Flash Flood Forecasting: What Are the Limits of Predictability? Q. J. R. Meteorol. Soc.
**2007**, 133, 3–23. [Google Scholar] [CrossRef] - Elmahdy, S.; Ali, T.; Mohamed, M. Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach. J. Remote Sens.
**2020**, 12, 2695. [Google Scholar] [CrossRef] - Costache, R.; Ngo, P.T.T.; Bui, D.T. Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping. Water
**2020**, 12, 1549. [Google Scholar] [CrossRef] - Costache, R.; Barbulescu, A.; Pham, Q.B. Integrated Framework for Detecting the Areas Prone to Flooding Generated by Flash-Floods in Small River Catchments. Water
**2021**, 13, 758. [Google Scholar] [CrossRef] - Zaharia, L.; Costache, R.; Prăvălie, R.; Ioana-Toroimac, G. Mapping flood and flooding potential indices: A methodological approach to identifying areas susceptible to flood and flooding risk. Case study: The Prahova catchment (Romania). Front. Earth Sci.
**2017**, 11, 229–247. [Google Scholar] [CrossRef] - Costache, R.; Hong, H.; Wang, Y. Identification of torrential valleys using GIS and a novel hybrid integration of artificial intelligence, machine learning and bivariate statistics. Catena
**2019**, 183, 104179. [Google Scholar] [CrossRef] - Costache, R.; Pham, Q.B.; Arabameri, A.; Diaconu, D.C.; Costache, I.; Crăciun, A.; Ciobotaru, N.; Pandey, M.; Arora, A.; Ali, S.A.; et al. Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles with multicriteria decision making and machine learning. Geocarto Int.
**2021**. [Google Scholar] [CrossRef] - Bui, D.T.; Ngo, P.T.T.; Pham, T.D.; Jaafari, A.; Minh, N.Q.; Hoa, P.V.; Samui, P. A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena
**2019**, 179, 184–196. [Google Scholar] [CrossRef] - Bui, D.T.; Hoang, N.D.; Pham, T.D.; Ngo, P.T.T.; Hoa, P.V.; Minh, N.Q.; Tran, X.T.; Samui, P. A new intelligence approach based on GIS-based Multivariate Adaptive Regression Splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area. J. Hydrol.
**2019**, 575, 314–326. [Google Scholar] - Ngo, P.T.T.; Hoang, N.D.; Pradhan, B.; Nguyen, Q.K.; Tran, X.T.; Nguyen, Q.M.; Nguyen, V.N.; Samui, P.; Tien Bui, D. A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data. Sensors
**2018**, 18, 3704. [Google Scholar] [CrossRef] [Green Version] - Ma, M.; Zhang, J.; Su, H.; Wang, D.; Wang, Z. Update of Early Warning Indicators of Flash Floods: A Case Study of Hunjiang District, Northeastern China. Water
**2019**, 11, 314. [Google Scholar] [CrossRef] [Green Version] - Wang, Y.; Liu, R.; Guo, L.; Tian, J.; Zhang, X.; Ding, L.; Wang, C.; Shang, Y. Forecasting and Providing Warnings of Flash Floods for Ungauged Mountainous Areas Based on a Distributed Hydrological Model. Water
**2017**, 9, 776. [Google Scholar] [CrossRef] - Zhai, X.; Guo, L.; Liu, R.; Zhang, Y. Rainfall threshold determination for flash flood warning in mountainous catchments with consideration of antecedent soil moisture and rainfall pattern. Nat. Hazards
**2018**, 94, 605–625. [Google Scholar] [CrossRef] - Liu, C.J.; Ma, Q.; Zhang, X.; Li, C.; Li, Q.; Gourbesville, P.; Guo, L.; Ding, L. Identification and quantitative analysis of flash flood risks for small catchments in China: A new operational modelling approach. LHB-Hydrosci. J.
**2022**, 108, 2019561. [Google Scholar] [CrossRef] - Ngo, P.T.T.; Pham, T.D.; Hoang, N.D.; Tran, D.A.; Amiri, M.; Le, T.T.; Hoa, P.V.; Van Bui, P.; Nhu, V.H.; Bui, D.T. A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping. J. Environ. Manag.
**2021**, 280, 111858. [Google Scholar] [CrossRef] - Bui, Q.T.; Nguyen, Q.H.; Nguyen, X.L.; Pham, V.D.; Nguyen, H.D.; Pham, V.M. Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. J. Hydrol.
**2020**, 581, 124379. [Google Scholar] [CrossRef] - Pande, R.K. Flash flood disasters in Uttarakhand. J. Disaster Prev. Manag.
**2010**, 19, 565–570. [Google Scholar] [CrossRef] - Singh, G.; Pandey, A. Flash flood vulnerability assessment and zonation through an integrated approach in the Upper Ganga Basin of the Northwest Himalayan region in Uttarakhand. Int. J. Disaster Risk Reduct.
**2021**, 66, 102573. [Google Scholar] [CrossRef] - Singh, G.; Pandey, A. Hybrid ensemble modeling for flash flood potential assessment and susceptibility analysis of a Himalayan river catchment. Geocarto Int.
**2021**. [Google Scholar] [CrossRef] - Bui, D.T.; Hoang, N.D.; Martínez-Álvarez, F.; Ngo, P.T.T.; Hoa, P.V.; Pham, T.D.; Costache, R. A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Sci. Total Environ.
**2020**, 701, 134413. [Google Scholar] - Lindell, M.K.; Arlikatti, S.; Huang, S.K. Immediate behavioral response to the June 17, 2013 flash floods in Uttarakhand, North India. Int. J. Disaster Risk Reduct.
**2019**, 34, 129–146. [Google Scholar] [CrossRef] - Gazni, T.A. An international comparison of journal publishing and citing behaviours. J. Informetr.
**2012**, 6, 516–531. [Google Scholar] - Kim, E.S.; Choi, H.I. Estimation of the relative severity of floods in small ungauged catchments for preliminary observation on Flash flood preparedness: A case study in Korea. Int. J. Environ. Res. Public Health
**2012**, 9, 1507–1522. [Google Scholar] [CrossRef] [Green Version] - Shirzadi, A.; Asadi, S.; Shahabi, H.; Ronoud, S.; Clague, J.J.; Khosravi, K.; Pham, B.T.; Ahmad, B.B.; Bui, D.T. A novel ensemble learning based on Bayesian Belief Network coupled with an extreme learning machine for flash flood susceptibility mapping. Eng. Appl. Artif. Intell.
**2020**, 96, 103971. [Google Scholar] [CrossRef] - Nhu, V.H.; Thi Ngo, P.T.; Pham, T.D.; Dou, J.; Song, X.; Hoang, N.D.; Tran, D.A.; Cao, D.P.; Aydilek, İ.B.; Amiri, M.; et al. A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping. Remote Sens.
**2020**, 12, 2688. [Google Scholar] [CrossRef] - Ettinger, S.; Mounaud, L.; Magill, C.; Yao-Lafourcade, A.F.; Thouret, J.C.; Manville, V.; Negulescu, C.; Zuccaro, G.; De Gregorio, D.; Nardone, S. Building vulnerability to hydro-geomorphic hazards: Estimating damage probability from qualitative vulnerability assessment using logistic regression. J. Hydrol.
**2016**, 541, 563–581. [Google Scholar] [CrossRef] - Trigila, A.; Iadanza, C.; Esposito, C.; Scarascia-Mugnozza, G. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology
**2015**, 249, 119–136. [Google Scholar] [CrossRef] - Yaseen, A.; Lu, J.; Chen, X. Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model. Stoch. Environ. Res. Risk Assess.
**2022**, 36, 3041–3061. [Google Scholar] [CrossRef] - Giovannettone, J.; Copenhaver, T.; Burns, M.; Choquette, S. A Statistical Approach to Mapping Flood Susceptibility in the Lower Connecticut River Valley Region. Water Resour. Res.
**2018**, 54, 7603–7618. [Google Scholar] [CrossRef] - Lee, S.; Pradhan, B. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. J. Landslides
**2007**, 4, 33–41. [Google Scholar] [CrossRef] - Oh, H.J.; Lee, S.; Soedradjat, G.M. Quantitative landslide susceptibility mapping at Pemalang area, Indonesia. J. Environ. Geol.
**2010**, 60, 1317–1328. [Google Scholar] [CrossRef] - Sharma, S.; Mahajan, A.K. A comparative assessment of information value, frequency ratio and analytical hierarchy process models for landslide susceptibility mapping of a Himalayan watershed, India. J. Bull. Eng. Geol. Environ.
**2018**, 78, 2431–2448. [Google Scholar] [CrossRef] - Aristizábal, E.; Arango Carmona, M.I.; Gómez, F.J.; López Castro, S.M.; De Villeros Severiche, A.; Riaño Quintanilla, A.F. Hazard Analysis of Hydrometeorological Concatenated Processes in the Colombian Andes. In Advances in Natural Hazards and Hydrological Risks: Meeting the Challenge; Advances in Science, Technology & Innovation; Springer: Cham, Switzerland, 2020; Volume 12, pp. 7–10. [Google Scholar]
- Hungr, O.; Leroueil, S.; Picarelli, L. The Varnes classification of landslide types, an update. Landslides
**2014**, 11, 167–194. [Google Scholar] [CrossRef] - Stolle, A.; Langer, M.; Bloethe, J.H.; Korup, O. On predicting debris flows in arid mountain belts. J. Glob. Planet. Change
**2015**, 126, 1–13. [Google Scholar] [CrossRef] - Santangelo, N.; Forte, G.; De Falco, M.; Chirico, G.B.; Santo, A. New insights on rainfall triggering flow-like landslides and flash floods in Campania (Southern Italy). Landslides
**2021**, 18, 2923–2933. [Google Scholar] [CrossRef] - Mei, Y.; Anagnostou, E.N.; Nikolopoulos, E.I.; Borga, M. Error Analysis of Satellite Precipitation Products in Mountainous Basins. J. Hydrometeorol.
**2014**, 15, 1778–1793. [Google Scholar] [CrossRef] - Karagiorgos, K.; Thaler, T.; Heiser, M.; Hübl, J.; Fuchs, S. Integrated flash flood vulnerability assessment: Insights from East Attica, Greece. J. Hydrol.
**2016**, 541, 553–562. [Google Scholar] [CrossRef] - White, H.D. Combining bibliometrics, information retrieval, and relevance theory: Part 1: First examples of a synthesis. J. Am. Soc. Inf. Sci. Technol.
**2007**, 58, 536–559. [Google Scholar] [CrossRef] - Tehrany, M.S.; Kumar, L.; Shabani, F. A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia. PeerJ
**2019**, 7, e7653. [Google Scholar] [CrossRef] [PubMed] - Tehrany, M.S.; Jones, S.; Shabani, F. Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. Catena
**2019**, 175, 174–192. [Google Scholar] [CrossRef] - Tehrany, M.S.; Pradhan, B.; Jebur, M.N. Flood Susceptibility Analysis and Its Verification Using a Novel Ensemble Support Vector Machine and Frequency Ratio Method. Stoch. Environ. Res. Risk Assess.
**2015**, 29, 1149–1165. [Google Scholar] [CrossRef] - Tehrany, M.S.; Lee, M.J.; Pradhan, B.; Jebur, M.N.; Lee, S. Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ. Earth Sci.
**2014**, 72, 4001–4015. [Google Scholar] [CrossRef] - Arabameri, A.; Saha, S.; Chen, W.; Roy, J.; Pradhan, B.; Bui, D.T. Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques. J. Hydrol.
**2020**, 587, 125007. [Google Scholar] [CrossRef] - Collaborators, I.P. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet
**2016**, 388, 1545–1602. [Google Scholar] - Afshin, A.; Sur, P.J.; Fay, K.A.; Cornaby, L.; Ferrara, G.; Salama, J.S.; Mullany, E.C.; Abate, K.H.; Abbafati, C.; Abebe, Z.; et al. Health effects of dietary risks in 195 countries, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet
**2019**, 393, 1958–1972. [Google Scholar] [CrossRef] - Zeng, Z.; Tang, G.; Long, D.; Zeng, C.; Ma, M.H.; Hong, Y.; Xu, H.; Xu, J. A cascading flash flood guidance system: Development and application in Yunnan Province, China. Nat Hazards
**2016**, 84, 2071–2093. [Google Scholar] [CrossRef] - Waqas, H.; Lu, L.; Tariq, A.; Li, Q.; Baqa, M.F.; Xing, J.; Sajjad, A. Flash Flood Susceptibility Assessment and Zonation Using an Integrating Analytic Hierarchy Process and Frequency Ratio Model for the Chitral District, Khyber Pakhtunkhwa, Pakistan. Water
**2021**, 13, 1650. [Google Scholar] [CrossRef] - Afreen, M.; Haq, F.; Mukhtar, Z. Flood susceptibility analysis of the Panjkora Valley Northern Pakistan, using frequencyratio approach. Int. J. Disaster Resil. Built Environ.
**2022**, 13, 601–614. [Google Scholar] [CrossRef] - Saleh, A.; Yuzir, A.; Sabtu, N.; Abujayyab, S.K.M.; Bunmi, M.R.; Pham, Q.B. Flash flood susceptibility mapping in urban area using genetic algorithm and ensemble method. Geocarto Int.
**2022**. [Google Scholar] [CrossRef] - Yu, C.; Chen, J. Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model. Symmetry
**2020**, 12, 1848. [Google Scholar] [CrossRef] - El-Fakharany, M.A.; Hegazy, M.N.; Mansour, N.M.; Abdo, A.M. Flash flood hazard assessment and prioritization of sub-watersheds in Heliopolis basin, East Cairo, Egypt. Arab J Geosci.
**2021**, 14, 1693. [Google Scholar] [CrossRef] - Karmokar, S.; De, M. Flash flood risk assessment for drainage basins in the Himalayan foreland of Jalpaiguri and Darjeeling Districts, West Bengal. Model. Earth Syst. Environ.
**2020**, 6, 2263–2289. [Google Scholar] [CrossRef] - Tehrany, M.S.; Pradhan, B.; Jebur, M.N. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J. Hydrol.
**2013**, 504, 69–79. [Google Scholar] - Alaa, M.A.A.; Shamsuddin, S.; Ali, K.A.A. A GIS-based integration of catastrophe theory and analytical hierarchy process for mapping flood susceptibility: A case study of Teeb area, Southern Iraq. Environ. Earth Sci.
**2016**, 75, 687. [Google Scholar] - Magnini, A.; Lombardi, M.; Persiano, S.; Tirri, A.; Conti, F.L.; Castellarin, A. Machine-Learning blends of geomorphic descriptors: Value and limitations for flood hazard assessment across large floodplains. J. Nat. Hazards Earth Syst. Sci.
**2022**, 22, 1469–1486. [Google Scholar] [CrossRef] - Dejen, A.; Soni, S. Flash flood risk assessment using geospatial technology in Shewa Robit town, Ethiopia. Model. Earth Syst. Environ.
**2021**, 7, 2599–2617. [Google Scholar] [CrossRef] - Chakrabortty, R.; Pal, S.C.; Rezaie, F.; Arabameri, A.; Lee, S.; Roy, P.; Saha, A.; Chowdhuri, I.; Moayedi, H. Flash-Flood Hazard Susceptibility Mapping in Kangsabati River Basin, India. Geocarto Int.
**2022**, 37, 6713–6735. [Google Scholar] [CrossRef] - Cao, C.; Xu, P.; Wang, Y.; Chen, J.; Zheng, L.; Niu, C. Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas. Sustainability
**2016**, 8, 948. [Google Scholar] [CrossRef] [Green Version] - Huang, W.; Cao, Z.X.; Qi, W.J.; Pender, G.; Zhao, K. Full 2D hydrodynamic modelling of rainfall-induced flash floods. J. Mt. Sci.
**2015**, 12, 1203–1218. [Google Scholar] [CrossRef] - Liu, J.; Wang, J.; Xiong, J.; Cheng, W.; Sun, H.; Yong, Z.; Wang, N. Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets. Remote Sens.
**2021**, 13, 4945. [Google Scholar] [CrossRef] - Ding, L.S.; Ma, L.; Li, L.G.; Liu, C.; Li, N.W.; Yang, Z.L.; Yao, Y.Z.; Lu, H. A Survey of Remote Sensing and Geographic Infor mation System Applications for Flash Floods. Remote Sens.
**2021**, 13, 1818. [Google Scholar] [CrossRef] - Rahman, M.; Ningsheng, C.; Islam, M.M.; Mahmud, G.I.; Pourghasemi, H.R.; Alam, M.; Rahim, M.A.; Baig, M.A.; Bhattacharjee, A.; Dewan, A. Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm. J. Clean. Prod.
**2021**, 311, 127594. [Google Scholar] [CrossRef] - Zhang, M.; Dai, Z.; Bouma, T.J.; Bricker, J.; Townend, I.; Wen, J.; Zhao, T.; Cai, H. Tidal-flat reclamation aggravates potential risk from storm impacts. Coast. Eng.
**2021**, 166, 103868. [Google Scholar] [CrossRef] - Mi, J.; Zhang, M.; Zhu, Z.; Vuik, V.; Wen, J.; Gao, H.; Bouma, T.J. Morphological Wave Attenuation of the Nature-Based Flood Defense: A Case Study from Chongming Dongtan Shoal, China. J. Sci. Total Environ.
**2022**, 831, 154813. [Google Scholar] [CrossRef] - Zhao, G.; Pang, B.; Xu, Z.; Yue, J.; Tu, T.; Pang, B.; Xu, Z.; Yue, J.; Tu, T. Mapping flood susceptibility in mountainous areas on a national scale in China. Sci. Total Environ.
**2018**, 615, 1133–1142. [Google Scholar] [CrossRef] - Abedi, R.; Costache, R.; Shafizadeh-Moghadam, H.; Pham, Q.B. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto Int.
**2022**, 37, 5479–5496. [Google Scholar] [CrossRef] - Costache, R.; Arabameri, A.; Blaschke, T.; Pham, Q.B.; Pham, B.T.; Pandey, M.; Arora, A.; Linh, N.T.T.; Costache, I. Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. Sensors
**2021**, 21, 280. [Google Scholar] [CrossRef] [PubMed] - Costache, R. Flash-flood Potential Index mapping using weights of evidence, decision Trees models and their novel hybrid integration. J. Stoch. Environ. Res. Risk Assess.
**2019**, 33, 1375–1402. [Google Scholar] [CrossRef] - Zhang, R.; Chen, Y.; Zhang, X.; Ma, Q.; Ren, L. Mapping homogeneous regions for flash floods using machine learning: A case study in Jiangxi province, China. Int. J. Appl. Earth Obs. Geoinf.
**2020**, 108, 102717. [Google Scholar] [CrossRef] - Kruczkiewicz, A.; Bucherie, A.; Ayala, F.; Hultquist, C.; Vergara, H.; Mason, S.; Bazo, J.; de Sherbinin, A. Development of a Flash Flood Confidence Index from Disaster Reports and Geophysical Susceptibility. Remote Sens.
**2021**, 13, 2764. [Google Scholar] [CrossRef] - Archer, D.; O’Donnell, G.; Lamb, R.; Warren, S.; Fowler, H.J. Historical Flash Floods in England: New Regional Chronologies and Database. J. Flood Risk Manag.
**2019**, 12, e12526. [Google Scholar] [CrossRef] - Kaiser, M.; Günnemann, S.; Disse, M. Providing guidance on efficient flash flood documentation: An application based approach. J Hydrol.
**2020**, 581, 124466. [Google Scholar] [CrossRef] - Muhamad, N.; Arshad, S.; Pereira, J.J. Exposure Elements in Disaster Databases and Availability for Local Scale Application: Case Study of Kuala Lumpur, Malaysia. Front. Earth Sci.
**2021**, 9, 616246. [Google Scholar] [CrossRef] - Su, M.; Ying, Z.; Yan, H.; Chen, Q.; Chen, S.; Chen, Z.; Hong, X. The influence of landscape pattern on the risk of urban water-logging and flood disaster. Ecol. Indic.
**2017**, 92, 133–140. [Google Scholar] [CrossRef] - Tian, Y.; Xiao, C.; Liu, Y.; Wu, L. Effects of Raster Resolution on Landslide Susceptibility Mapping: A Case Study of Shenzhen. Sci. China Ser. Technol. Sci.
**2008**, 51, 188–198. [Google Scholar] [CrossRef] - Baran-Zgłobicka, B.; Godziszewska, D.; Zgłobicki, W. The Flash Floods Risk in the Local Spatial Planning (Case Study: Lublin Upland, E Poland). Resources
**2021**, 10, 14. [Google Scholar] [CrossRef] - Ruidas, D.; Chakrabortty, R.; Islam, A.M.T.; Saha, A.; Pal, S.C. A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India. Environ. Earth Sci.
**2022**, 81, 145. [Google Scholar] [CrossRef] - Lin, J.; He, X.; Lu, S.; Liu, D.; He, P. Investigating the influence of three-dimensional building configuration on urban pluvial flooding using random forest algorithm. Environ. Res.
**2021**, 196, 110438. [Google Scholar] [CrossRef] [PubMed] - Hu, Q.; Zhu, Y.; Hu, H.; Guan, Z.; Qian, Z.; Yang, A. Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility. Water Resour. Manag.
**2022**, 36, 55–73. [Google Scholar] [CrossRef] - Bellos, V.; Papageorgaki, I.; Kourtis, I.; Vangelis, H.; Kalogiros, I.; Tsakiris, G. Reconstruction of a flash flood event using a 2D hydrodynamic model under spatial and temporal variability of storm. Nat. Hazards
**2020**, 101, 711–726. [Google Scholar] [CrossRef]

**Figure 10.**Diagram of pennant figures of the top four cited authors. (

**a**). Diagram of pennant figures of Tehrany. (

**b**). Diagram of pennant figures of Bui. (

**c**). Diagram of pennant figures of Khosravi. (

**d**). Diagram of pennant figures of Pradhan.

Method | Characteristics | Research | Content |
---|---|---|---|

Time sequence | Facilitate understanding of the evolution of the research field. | A review of advances in China’s flash flood early-warning system [53] | This paper reviewed the three stages of China’s flash flood early-warning system. |

Case study | Easy acquisition of key elements of the study. However, a large amount of data is needed to support the theory. | Flash flood susceptibility modeling: a review [54] | This paper summarized the modeling process of flash flood susceptibility by six researchers. |

PRISMA methodology | A clear literature search process; Standardized document analysis process; Objective analysis results. | Advances and challenges in flash flood risk assessment: A review [55] | This paper used the PRIAMA method to search nearly 60 articles in the Google scholar database to analyze the methods, uncertainties and challenges in flash flood risk assessment. |

Logical structure | Logical; The review methodology includes step-by-step analysis of research links in the field of study and categorical summaries, etc.; Easily identify the problems facing the field of study. | Flood risk assessment, future trend modeling, and risk communication: a review of ongoing research [56] | This paper first reviewed the common approaches to flood risk assessment. With the impact of climate change, population, and urbanization, this paper focused on the application of climate change and population and economic growth in risk assessment. |

A review of advances in flash flood forecasting [57] | This paper used the logical structure of flash flood forecasting and reviewed the development of quantitative precipitation estimation (QPE) and quantitative precipitation forecasting (QPF), remote sensing data products, and flow forecasting models in flash flood forecasting and uncertainty estimation. | ||

Flood risk assessments at different spatial scales [58] | This paper summarized the study of methods, use of assessments, and uncertainty in risk assessment at macroscopic and microscopic scales. | ||

A review on remote sensing and GIS applications to monitor natural disasters in Indonesia [59] | This paper was based on the logic sequence of image acquisition, classification, and map generation. |

Title | Year | Cite Frequency | Country/Regions | Keywords | The Research Content |
---|---|---|---|---|---|

Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models [74] | 2020 | 50 | Romania | Bâsca Chiojdului catchment; Flash-flood potential index; Statistical index; Machine learning models; Ensemble models | This study used a bivariate statistical method and multiple new ensemble models of multiple machine learning models to calculate the flash flood potential index in the Bâsca Chiojdului river basin. |

Flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan [75] | 2019 | 34 | Pakistan | Hindu Kush; Flash flood; Susceptibility model; Geo-morphometry; Surface run-off | This study divided the Panjkora Basin, Eastern Hindu Kush, Pakistan into 30 sub-basin units and used geo-morphometric and hydrological approaches to evaluate the flash flood susceptibility. |

Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China [44] | 2020 | 6 | China | Geodetector; Certainty factor; Flash flood susceptibility; GIS; Logistic regression; Fujian Province | This study used Pearson Correlation coefficient and Geodetector to screen and evaluate the index weight and developed an integrated model of deterministic factor and logistic regression to assess the flash flood susceptibility. |

Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods [76] | 2019 | 101 | Vietnam | Bagging and boosting models; FURIA; Genetic algorithms; Flash flood susceptibility; Vietnam | This study developed an integrated approach based on feature selection method (FSM) and tree for flash flood susceptibility. |

Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques [48] | 2020 | 77 | Romania | Flash-flood potential index; K-Star; k-Nearest Neighbor; Analytical hierarchy process; Prahova river catchment; Machine learning | This study evaluated the performance of the Analytical Hierarchy Process (AHP), fi (kNN), and K-Star (KS) algorithms and their ensembles for flash flood susceptibility mapping with statistical indicators: sensitivity, specificity, and accuracy. |

Title | Total Citations | Year | Research Purpose | Research Contents |
---|---|---|---|---|

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran [78] | 300 | 2018 | Flash flood susceptibility mapping | The flash flood disaster susceptibility assessment at Haraz watershed, northern Iran was made by four machine learning hybrid models. |

Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates [79] | 249 | 2011 | Flood damage uncertainty estimation | The study assessed the impact of uncertainty in inundation depth, land use, the value of risk elements, and sensitive hydrological conditions on flood damage estimates. |

Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution [80] | 179 | 2018 | Flood susceptibility assessment | The flood susceptibility assessment in Hengfeng County, China was made respectively by the hybrid model of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm, and the hybrid model of adaptive neuro-fuzzy inference system (ANFIS) and differential evolution, showing that the hybrid model of ANFIS-DE has higher accuracy. |

Flash flood forecasting: What are the limits of predictability? [82] | 172 | 2007 | Flash flood forecasting method | The method of forecasting flash floods under the limitations and uncertainties of meteorology and hydrology in the forecasting system was elaborated. |

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis [81] | 159 | 2016 | Identify flood risk areas | The analytic hierarchy process (AHP) was used to identify flood risk areas, which showed that the method has high accuracy. |

Number | Keywords | Count | Year | Number | Keywords | Count | Year |
---|---|---|---|---|---|---|---|

1 | flash flood | 92 | 2012 | 11 | climate change | 38 | 2012 |

2 | model | 76 | 2007 | 12 | risk assessment | 36 | 2007 |

3 | susceptibility | 51 | 2012 | 13 | vulnerability | 34 | 2010 |

4 | risk | 50 | 2010 | 14 | prediction | 32 | 2007 |

5 | logistic regression | 47 | 2012 | 15 | weights of evidence | 31 | 2016 |

6 | hazard | 43 | 2010 | 16 | river basin | 30 | 2016 |

7 | spatial prediction | 43 | 2018 | 17 | support vector machine | 25 | 2016 |

8 | area | 41 | 2012 | 18 | susceptibility assessment | 25 | 2016 |

9 | landslide susceptibility | 40 | 2010 | 19 | classification | 24 | 2012 |

10 | frequency ratio | 39 | 2016 | 20 | debris flow | 24 | 2013 |

Institutions | Centrality | Count |
---|---|---|

Duy Tan University | 0.09 | 26 |

University of Bucharest | 0.10 | 24 |

Natl Inst Hydrol & Water Management | 0.08 | 18 |

Ton Duc Thang University | 0.02 | 17 |

Chinese Acad Sci | 0.04 | 15 |

Scale | The Researchers | Research Area | The Research Methods | |
---|---|---|---|---|

Administrative division | Province | Zeng et al. [128] | Yunnan Province, China | This study developed a cascade flash flood warning (CFFG) system, including flash flood potential index (FFPI), flash flood hazard index (FFHI), and flash flood risk index (FFRI), and showed that the counties in eastern Yunnan were the most vulnerable to mountain floods. |

City/Area | Waqas et al. [129] | Chitral in Khyber Pakhtunkhwa (KPK), Pakistan | This study used a comprehensive analysis method and frequency ratio model to evaluate flash flood susceptibility. | |

Watershed | Watershed | Band et al. [33] | the Kalvan watershed in Markazi Province, Iran | This study used five machine learning models to assess the flash flood susceptibility and identify flood-prone areas in the study area. |

Basin | Afreen et al. [130] | Panjkora Basin | This study used a frequency ratio model to identify flood-prone areas in the Panjkora Basin. | |

Disaster-affected site | Disaster-affected site | Saleh et al. [131] | 110 flash flood locations in the Sungai Pinang catchment | This study used four integrated machine learning models: statistical index-logistic regression (SI-LR), statistical index-k-nearest neighbor (SI-KNN), statistical index-extreme Gradient Boosting (SI-XGBoost), and statistical index-Random Forest (SI-RF) to map flash flood susceptibility. |

Unit Type | Methods | The Advantages and Disadvantages |
---|---|---|

Grid unit | The study area is divided into squares of uniform size based on DEM, and then a matrix is formed for calculation. | a. Convenient calculation and division; b. Cannot reflect the unit geology and topography. |

Slope unit | The region near similar terrain is divided into a nearly homogeneous unit. | a. Reflect geological and topographical conditions; b. Integrity. |

Topographical unit | The continuous terrain surface is divided into several discrete terrain units with differences. | a. Reflect the geology; b. The higher the precision, the greater the number of units and the more complex the work. |

Unique condition unit | Overlay the assessment factor data layers to obtain multiple irregular assessment units of different sizes. | a. It is suitable for the study of large regional scale; b. High computational difficulty. |

Geomorphic unit | The internal homogeneity and external difference of geomorphic types were maximized, and geomorphic units were taken as the assessment units. | a. Reflect unit geology; b. The division accuracy depends on the professional height. |

The Researchers | Research Area | Unit Size |
---|---|---|

Youssef et al. [23] | Five Wadis in Jeddah, Saudi Arabia | 5 × 5 m |

Alireza et al. [125] | Gorgan Basin, Iran | 12.5 × 12.5 m |

Tehrany et al. [135] | the part of Kelantan River Basin in North East part of Peninsular Malaysia | 15 × 15 m |

Pham, B.T et al. [36] | Nghe An Province, north central coast region of Vietnam | 20 × 20 m |

Al-Abadi et al. [136] | Northeastern Missan province in southern Iraq | 30 × 30 m |

Magnini, A. et al. [137] | Most of northern Italy and a small part of Switzerland | 90 × 90 m |

The Researchers | The Assessment Index | Screening Method for Correlation between Factors | Screening of Correlation between Factors and Flash Flood | The Final Assessment Index |
---|---|---|---|---|

Cao et al. [44] | Elevation, Slope, Topographic relief, NDVI, Land use type, Soil type, Soil depth, Distance from rivers, 6 h precipitation (H6_100), 24 h precipitation (H24_100), Annual rainfall, Tropical cyclone index, Population density, Economic density. | Pearson correlation coefficient | Geodetector | H24_100, Annual rainfall, Topographic relief, Elevation, NDVI, Land use type, Population density, Tropical cyclone index. |

Pham, B.T. et al. [45] | Distance from rivers, Aspect, Elevation, Slope, Rainfall, Distance from faults, Soil, Land use, Lithology. | - | Correlation based feature selection method | Distance from rivers, Slope, Elevation, Lithology, Soil, Rainfall, Land use, Aspect. |

Costache et al. [74] | Slope, L-S factor, Land use/Land cover, TWI, Convergence index, Lithology, Hydrological soil group, Stream Power Index (SPI), TPI, Profile curvature. | Variance Inflation of multicollinearity investigation (VIF), Tolerance, Information Gain method | - | Slope, TPI, TWI, Land use, Lithology, L-S Factor, Convergence index, Hydrological soil groups |

Liu et al. [18] | Flash flood intensity, Ele and Slp, Soil, P5, P50, and P100, Vegetation Fractional Cover (VFC), POP, Pcul, and GDP, Variation in the flash flood intensity, Changes in precipitation, human activity factors. | Geographic detector (interaction detector) | Geographic detector (factor detector) | VFC, POP, Pcul, and GDP, P5, P50, and P100, Human activity, Precipitation. |

Research Models | Research Area | Methods | Conclusions |
---|---|---|---|

Intelligent learning model | Kangsabati watershed, India [139] | The three models of particle swarm optimization (PSO), artificial neural network (ANN), and deep learning neural network (DLNN) were used for mapping the flash flood sensitivity in the research area, and classifying the flash flood prone areas in the research area. | Among the three models, PSO model performed well, and can be used for the landscape sensitivity mapping in subtropical areas, eastern India. |

Statistical model | Xiqu Gully, Beijing, China [140] | Frequency ratio (FR) and statistical index (SI) models were used to generate the flash flood sensitivity mapping in the research area. | FR model was more suitable for the flash flood sensitivity mapping in this area. |

Hydrological model | Panjkora Basin, Eastern Hindu Kush, Pakistan [67] | The surface morphology hierarchical model and NRCS-CN hydrological model were used for estimating the flash flood susceptibility in the research area and mapping the flash flood sensitivity in the research area in combination with the parameters of surface morphology, hydrology, geology, and land cover in GIS environment. | The flash flood sensitivity mapping can be well done by this method. |

Hydrodynamic model | Lengkou catchment, Shanxi Province, China [141] | A hydrodynamic model of flash floods based on two-dimensional shallow water equation (2D-SWE), including rainfall and infiltration parameters was proposed in this research to simulate two flash floods in the research area. | The model predicted the scale of flash floods more accurately, and can predict large-scale flash floods. |

Hybrid model | Bac Ha Bao Yen (BHBY) region in northwest Vietnam [91] | A new hybrid machine learning method was proposed in this research for spatial prediction of flash floods in the research area. | The hybrid model has high efficiency and is a new method for predicting flash flood susceptibility. |

Error Statistical Index | Statistical Indexes | Calculation Method |
---|---|---|

SST (%) [21,84] | The closer the value is to 100, the more accurate the model is. | $SST=\frac{A}{A+D}$ [49] |

SPF (%) [21,84] | The closer the value is to 100, the more accurate the model is. | $SPF=\frac{C}{C+B.}$ [49] |

ACC (%) [48,105] | The closer the value is to 100, the more accurate the model is. | $ACC=\frac{A+C}{A+C+B+D}$ [49] |

RMSE [144,145,146] | The closer the value is to 0, the more accurate the model is. | $RMSE=\sqrt{{\displaystyle \sum _{i=1}^{n}\frac{{\left({y}_{i}-{t}_{i}\right)}^{2}}{n}}}$ [49] |

MAE [144,147] | The closer the value is to 0, the more accurate the model is. | $MAE=\frac{1}{n}{\displaystyle \sum _{i=1}^{n}\left|{y}_{i}-{t}_{i}\right|}$ [89] |

Kappa statistics [34,47,84] | The closer the value is to 1, the more accurate the model is. | $K=\frac{{P}_{o}-{P}_{est}}{1-{P}_{est}}{P}_{o}=(A+C){P}_{est}=(A+D)\times (A+D)+(B+C)\times (D+C)$ [49] |

R^{2} [89] | The closer the value is to 1, the more accurate the model is. | ${R}^{2}=1-\frac{{\displaystyle \sum _{i=1}^{n}{\left({y}_{i}-{t}_{i}\right)}^{2}}}{{{\displaystyle \sum _{i=1}^{n}\left({t}_{i}-\stackrel{-}{t}\right)}}^{2}}$ [89] |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Duan, L.; Liu, C.; Xu, H.; Huali, P.; Liu, H.; Yan, X.; Liu, T.; Yang, Z.; Liu, G.; Dai, X.;
et al. Susceptibility Assessment of Flash Floods: A Bibliometrics Analysis and Review. *Remote Sens.* **2022**, *14*, 5432.
https://doi.org/10.3390/rs14215432

**AMA Style**

Duan L, Liu C, Xu H, Huali P, Liu H, Yan X, Liu T, Yang Z, Liu G, Dai X,
et al. Susceptibility Assessment of Flash Floods: A Bibliometrics Analysis and Review. *Remote Sensing*. 2022; 14(21):5432.
https://doi.org/10.3390/rs14215432

**Chicago/Turabian Style**

Duan, Le, Chao Liu, Hui Xu, Pan Huali, Haizhi Liu, Xufeng Yan, Tiegang Liu, Zhengli Yang, Gang Liu, Xiaoai Dai,
and et al. 2022. "Susceptibility Assessment of Flash Floods: A Bibliometrics Analysis and Review" *Remote Sensing* 14, no. 21: 5432.
https://doi.org/10.3390/rs14215432