Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.4
Journals
Water
Special Issues
Advances in Flood Frequency and Inundation Modeling: Application of Statistical,...
share announcement

Advances in Flood Frequency and Inundation Modeling: Application of Statistical, Hydrodynamic, Remote Sensing, and Machine Learning Tools

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 23277

Special Issue Editors

Dr. Fazlul Karim

E-Mail Website
Guest Editor
CSIRO Land & Water, Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT 2601, Australia
Interests: hydrology; floodplain hydraulics; inundation modelling; water resources assessment; sediment transport; hydrological connectivity and linking hydrology and ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Zaved Khan

E-Mail Website
Guest Editor
Bureau of Meteorology, Research to Operations Program, Melbourne, Australia
Interests: hydrology; flood frequency analysis; seasonal rainfall forecasting; flood forecasting; statistical modeling; rainwater harvesting; uncertainty modeling
Prof. Dr. Tawatchai Tingsanchali

E-Mail
Guest Editor
Asian Institute of Technology, Pathumthani, Thailand
Interests: flood modelling; disaster management; water resources engineering and management; river hydraulics and sediment transport; surface water and groundwater hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is well recognized that floods are one of the deadliest natural disasters in the earth. Improved knowledge of flood frequency, duration, and inundation is a prerequisite for disaster management, infrastructure development, and environmental integrity. With recent advancements in computational methods and computing facilities, flood indicators are now estimated more accurately and efficiently.

We invite original research articles that contribute to the continuing efforts to understand complex hydrological and hydraulic processes and accurately estimate flood frequency, duration, inundation, and waterbody connectivity. This Special Issue also welcomes manuscripts on uncertainty analysis and application of flood modeling to support decision making.  

The topics for this Special Issue include but are not limited to:

  • Flood frequency analysis: advances in methods, regional case studies, variability, and trend analysis;
  • Inundation modeling: advances in computational methods and computing facilities comparison between methods and models;
  • Inundation mapping: advances in remote sensing techniques, strength/limitations of satellite data (e.g., MODIS, Landsat, Sentinel);
  • Integration of remote sensing and hydrodynamic modeling;
  • Flood hazard assessment and risk mapping;
  • Impacts of climate change on flood magnitude and frequency;
  • Sea-level rise and coastal flooding;
  • Uncertainty in flood modeling;
  • Application of machine learning tools for flood inundation modeling.

Dr. Fazlul Karim
Dr. Zaved Khan
Prof. Dr. Tawatchai Tingsanchali
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

14 pages, 2363 KiB  
Article
Extreme Rainfall Event Classification Using Machine Learning for Kikuletwa River Floods
by Lawrence Mdegela, Esteban Municio, Yorick De Bock, Edith Luhanga, Judith Leo and Erik Mannens
Water 2023, 15(6), 1021; https://doi.org/10.3390/w15061021 - 08 Mar 2023
Cited by 2 | Viewed by 2613
Abstract
Advancements in machine learning techniques, availability of more data sets, and increased computing power have enabled a significant growth in a number of research areas. Predicting, detecting, and classifying complex events in earth systems which by nature are difficult to model is one [...] Read more.
Advancements in machine learning techniques, availability of more data sets, and increased computing power have enabled a significant growth in a number of research areas. Predicting, detecting, and classifying complex events in earth systems which by nature are difficult to model is one such area. In this work, we investigate the application of different machine learning techniques for detecting and classifying extreme rainfall events in a sub-catchment within the Pangani River Basin, found in Northern Tanzania. Identification and classification of extreme rainfall event is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify a rain condition in the selected sub-catchment, we use data from five weather stations that have been labeled for the whole sub-catchment. In order to assess which machine learning technique is better suited for rainfall classification, we apply five different algorithms in a historical dataset for the period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, reporting random forest and XGBoost as having the best overall performances. However, because the class distribution is imbalanced, a generic multi-layer perceptron performs best when identifying heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the Pangani River Basin. Full article
(This article belongs to the Special Issue Advances in Flood Frequency and Inundation Modeling: Application of Statistical, Hydrodynamic, Remote Sensing, and Machine Learning Tools)
►▼ Show Figures

Figure 1

25 pages, 9729 KiB  
Article
Long-Term Temporal Flood Predictions Made Using Convolutional Neural Networks
by Hau-Wei Wang, Gwo-Fong Lin, Chih-Tsung Hsu, Shiang-Jen Wu and Samkele Sikhulile Tfwala
Water 2022, 14(24), 4134; https://doi.org/10.3390/w14244134 - 19 Dec 2022
Cited by 2 | Viewed by 1839
Abstract
This study proposes a method for predicting the long-term temporal two-dimensional range and depth of flooding in all grid points by using a convolutional neural network (CNN). The deep learning model was trained using a large rainfall dataset obtained from actual flooding events, [...] Read more.
This study proposes a method for predicting the long-term temporal two-dimensional range and depth of flooding in all grid points by using a convolutional neural network (CNN). The deep learning model was trained using a large rainfall dataset obtained from actual flooding events, and the corresponding raster flood data computed using a physical model. Various rainfall distributions (at different times or over different accumulation periods), the mesh of the simulated area, and the topography of the simulated area were considered when evaluating the performance of two CNNs: a simple CNN and Inception CNN. Neither CNN architecture could converge when the coordinate information was not included in the input data. Adding terrain elevation information to the rainfall data already containing coordinates increased the accuracy of flood prediction. Our findings indicated that in the proposed method, real-time flooding observation data are not required for corrections, and we concluded that the method can be used for long-term flood forecasting. Our model can accurately pinpoint when the water level changes from rising to falling. Once meteorological forecasted rainfall data are obtained, a corresponding long-term forecast of the two-dimensional flooding range and depth can be obtained within seconds. Full article
(This article belongs to the Special Issue Advances in Flood Frequency and Inundation Modeling: Application of Statistical, Hydrodynamic, Remote Sensing, and Machine Learning Tools)
►▼ Show Figures

Figure 1

18 pages, 7800 KiB  
Article
Harmonizing and Extending Fragmented 100 Year Flood Hazard Maps in Canada’s Capital Region Using Random Forest Classification
by Shelina A. Bhuiyan, Clement P. Bataille and Heather McGrath
Water 2022, 14(23), 3801; https://doi.org/10.3390/w14233801 - 22 Nov 2022
Cited by 1 | Viewed by 2176
Abstract
With the record breaking flood experienced in Canada’s capital region in 2017 and 2019, there is an urgent need to update and harmonize existing flood hazard maps and fill in the spatial gaps between them to improve flood mitigation strategies. To achieve this [...] Read more.
With the record breaking flood experienced in Canada’s capital region in 2017 and 2019, there is an urgent need to update and harmonize existing flood hazard maps and fill in the spatial gaps between them to improve flood mitigation strategies. To achieve this goal, we aim to develop a novel approach using machine learning classification (i.e., random forest). We used existing fragmented flood hazard maps along the Ottawa River to train a random forest classification model using a range of flood conditioning factors. We then applied this classification across the Capital Region to fill in the spatial gaps between existing flood hazard maps and generate a harmonized high-resolution (1 m) 100 year flood susceptibility map. When validated against recently produced 100 year flood hazard maps across the capital region, we find that this random forest classification approach yields a highly accurate flood susceptibility map. We argue that the machine learning classification approach is a promising technique to fill in the spatial gaps between existing flood hazard maps and create harmonized high-resolution flood susceptibility maps across flood-vulnerable areas. However, caution must be taken in selecting suitable flood conditioning factors and extrapolating classification to areas with similar characteristics to the training sites. The resulted harmonized and spatially continuous flood susceptibility map has wide-reaching relevance for flood mitigation planning in the capital region. The machine learning approach and flood classification optimization method developed in this study is also a first step toward Natural Resources Canada’s aim of creating a spatially continuous flood susceptibility map across the Ottawa River watershed. Our modeling approach is transferable to harmonize flood maps and fill in spatial gaps in other regions of the world and will help mitigate flood disasters by providing accurate flood data for urban planning. Full article
(This article belongs to the Special Issue Advances in Flood Frequency and Inundation Modeling: Application of Statistical, Hydrodynamic, Remote Sensing, and Machine Learning Tools)
►▼ Show Figures

Figure 1

23 pages, 2394 KiB  
Article
Flood Models: An Exploratory Analysis and Research Trends
by Fernando Morante-Carballo, Néstor Montalván-Burbano, Mijaíl Arias-Hidalgo, Luis Domínguez-Granda, Boris Apolo-Masache and Paúl Carrión-Mero
Water 2022, 14(16), 2488; https://doi.org/10.3390/w14162488 - 12 Aug 2022
Cited by 10 | Viewed by 3929
Abstract
Floods can be caused by heavy rainfall and the consequent overflow of rivers, causing low-lying areas to be affected. Populated regions close to riverbeds are the sectors most affected by these disasters, which requires modelling studies to generate different scenarios. The work focuses [...] Read more.
Floods can be caused by heavy rainfall and the consequent overflow of rivers, causing low-lying areas to be affected. Populated regions close to riverbeds are the sectors most affected by these disasters, which requires modelling studies to generate different scenarios. The work focuses on the bibliometric analysis of the search for topics such as flood modelling focused on the research, risk, and assessment of these catastrophes, aiming to determine new trends and tools for their application in the prevention of these natural disasters. The methodology consists of: (i) search criteria and database selection, (ii) pre-processing of the selected data and software, and (iii) analysis and interpretation of the results. The results show a wide range of studies for dimensional analysis in different flood scenarios, which greatly benefit the development of flood prevention and risk strategies. In addition, this work provides insight into the different types of software and modelling for flood analysis and simulation and the various trends and applications for future modelling. Full article
(This article belongs to the Special Issue Advances in Flood Frequency and Inundation Modeling: Application of Statistical, Hydrodynamic, Remote Sensing, and Machine Learning Tools)
►▼ Show Figures

Figure 1

28 pages, 5444 KiB  
Article
Comprehensive Assessment of Flood Hazard, Vulnerability, and Flood Risk at the Household Level in a Municipality Area: A Case Study of Nan Province, Thailand
by Tawatchai Tingsanchali and Thanasit Promping
Water 2022, 14(2), 161; https://doi.org/10.3390/w14020161 - 08 Jan 2022
Cited by 9 | Viewed by 3314
Abstract
Estimating flood hazard, vulnerability, and flood risk at the household level in the past did not fully consider all relevant parameters. The main objective of this study is to improve this drawback by developing a new comprehensive and systematic methodology considering all relevant [...] Read more.
Estimating flood hazard, vulnerability, and flood risk at the household level in the past did not fully consider all relevant parameters. The main objective of this study is to improve this drawback by developing a new comprehensive and systematic methodology considering all relevant parameters and their weighting factors. This new methodology is applied to a case study of flood inundation in a municipal area of Nan City in the Upper Nan River Basin in Thailand. Field and questionnaire surveys were carried out to collect pertinent data for input into the new methodology for estimating flood hazard, vulnerability, and risk. Designed floods for various return periods were predicted using flood simulation models for assessing flood risk. The flood risk maps constructed for the return periods of 10–500 years show a substantial increase in flood risk with the return periods. The results are consistent with past flood damages, which were significant near and along the riverbanks where ground elevation is low, population density is high, and the number of household properties are high. In conclusion, this new comprehensive methodology yielded realistic results and can be used further to assess the effectiveness of various proposed flood mitigation measures. Full article
(This article belongs to the Special Issue Advances in Flood Frequency and Inundation Modeling: Application of Statistical, Hydrodynamic, Remote Sensing, and Machine Learning Tools)
►▼ Show Figures

Figure 1

24 pages, 15152 KiB  
Article
Dynamic Assessment of the Flood Risk at Basin Scale under Simulation of Land-Use Scenarios and Spatialization Technology of Factor
by Jun Liu, Jiyan Wang, Junnan Xiong, Weiming Cheng, Xingjie Cui, Wen He, Yufeng He, Yu Duan, Gang Yang and Nan Wang
Water 2021, 13(22), 3239; https://doi.org/10.3390/w13223239 - 15 Nov 2021
Cited by 8 | Viewed by 2522
Abstract
Climate change, population increase, and urban expansion have increased the risk of flooding. Therefore, accurately identifying future changing patterns in the flood risk is essential. For this purpose, this study elaborated a new framework for a basin scale that employs a future land-use [...] Read more.
Climate change, population increase, and urban expansion have increased the risk of flooding. Therefore, accurately identifying future changing patterns in the flood risk is essential. For this purpose, this study elaborated a new framework for a basin scale that employs a future land-use simulation model, a factor spatialization technique, and a novel hybrid model for scenario-based flood risk assessment in 2030 and 2050. Three land-use scenarios (i.e., natural growth scenario, cropland protection scenario, and ecological protection scenario) were set and applied in Jinjiang Basin to explore the changes in future flood risk under these scenarios. The results indicate the different degrees of increase in flood risk that will occur in the three scenarios. Under the natural growth (NG) scenario, the city will expand rapidly with the growth of population and economy, and the total area with high and very high flood risk will increase by 371.30 km2 by 2050, as compared to 2020. However, under the ecological protection (EP) scenario, woodlands will be protected, and the growth in population, economy, and built-up lands will slow down with slightly increased risk of flooding. In this scenario, the total area with high and very high flood risk will increase by 113.75 km2 by 2050. Under the cropland protection (CP) scenario, the loss of croplands will have been effectively stopped, and the flood risk will not show a significant increase under this scenario, with an increase by only 90.96 km2 by 2050, similar to the EP scenario. Spatially, these increased flood risks mainly locate at the periphery of existing built-up lands, and the high-flood-risk zones are mainly distributed in the southeast of the Jinjiang Basin. The information about increasing flood risk determined by the framework provides insight into the spatio-temporal characteristics of future flood-prone areas, which facilitates reasonable flood mitigation measures to be developed at the most critical locations in the region. Full article
(This article belongs to the Special Issue Advances in Flood Frequency and Inundation Modeling: Application of Statistical, Hydrodynamic, Remote Sensing, and Machine Learning Tools)
►▼ Show Figures

Figure 1

Review

Jump to: Research

21 pages, 1245 KiB  
Review
A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling
by Fazlul Karim, Mohammed Ali Armin, David Ahmedt-Aristizabal, Lachlan Tychsen-Smith and Lars Petersson
Water 2023, 15(3), 566; https://doi.org/10.3390/w15030566 - 01 Feb 2023
Cited by 23 | Viewed by 5791
Abstract
Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep learning (DL) approaches have gained more attention across the [...] Read more.
Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep learning (DL) approaches have gained more attention across the world. In this paper, we reviewed recently published literature on ML and DL applications for flood modeling for various hydrologic and catchment characteristics. Our extensive literature review shows that DL models produce better accuracy compared to traditional approaches. Unlike physically based models, ML/DL models suffer from the lack of using expert knowledge in modeling flood events. Apart from challenges in implementing a uniform modeling approach across river basins, the lack of benchmark data to evaluate model performance is a limiting factor for developing efficient ML/DL models for flood inundation modeling. Full article
(This article belongs to the Special Issue Advances in Flood Frequency and Inundation Modeling: Application of Statistical, Hydrodynamic, Remote Sensing, and Machine Learning Tools)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop