Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.9
Journals
Sustainability
Special Issues
Natural Disasters: Modelling, Monitoring, Management and Mitigation Procedures
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Natural Disasters: Modelling, Monitoring, Management and Mitigation Procedures

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5597

Special Issue Editors

Dr. Stavros Sakellariou

E-Mail Website
Guest Editor
Department of Environmental Sciences, University of Thessaly, 41500 Larissa, Greece
Interests: natural disaster prevention and management; risk management; geographic information systems and remote sensing; natural disaster simulation and spatial resilience; spatial planning and climate change; sustainable development and urban spatial planning
Dr. Lampros Vasiliades

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Thessaly, 38334 Volos, Greece
Interests: geographic information systems; hydrology; management of extreme hydrological phenomena; hydrologic modelling and forecasting; spatial analysis techniques and remote sensing applications in civil engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Olga Christopoulou

E-Mail Website
Guest Editor
Department of Planning and Regional Development, University of Thessaly, 38334 Volos, Greece
Interests: sustainable development of rural areas; rural policy; environmental policy; stakeholders’ attitudes; development of mountainous areas; mountain tourism; sustainable tourism; socio-economic characteristics of rural areas; protected areas (Natura 2000 sites); natural resources and ecosystems management

Special Issue Information

Dear Colleagues,

In recent decades, natural disasters have caused extensive damage to the environment and are responsible for extensive loss of life, damage to infrastructure and economic loss. Natural disaster frequency and severity are increasing, affecting a significant number of people worldwide. More than 9000 natural disaster events have occurred in the last 30 years (approximately 300 events each year), creating serious issues in public health (through direct and indirect impacts such as injuries, malnutrition and infectious diseases) as well as in public and private assets and infrastructure. The economic cost to countries affected by natural disasters from 1998 to 2017 has risen to almost USD 3 trillion. Climate change has a fundamental effect on natural disasters, since approximately 77% the total cost is related to climate-related disasters.

In this framework, the exposure of population and societies to natural disasters is increasing, proportionally affecting the total risk. Climatic, meteorological, hydrological and geophysical disasters compose the framework of disasters for which we need to establish either hard or soft engineering measures as well as risk determination studies focusing on the comprehensive prevention and management of these types of disasters. The projection of the frequency and severity of natural disasters in the future is of the utmost importance to enhance preparedness.

Therefore, resilient cities and regions should focus on strategies and specific measures to reduce their vulnerability to these types of disasters. This would enhance the sustainability of inhabited regions, promoting a safer and more attractive environment for people who live, work and visit any place.

Within this context, the aim of this Special Issue is to collect papers dealing with various aspects of natural disasters. The determination of disaster hazard, vulnerability and risk is key for the enhancement of preparedness and prevention. The spatial and temporal monitoring of any type of disaster could unveil certain spatiotemporal patterns. The modelling of disasters could provide decision makers with the appropriate information to establish the necessary preventative measures and adequately manage any type of disaster (e.g., evacuation route planning). The estimation of historic/future social and economic impacts due to the escalation of natural disasters would promote the interdisciplinary nature of the subject. The integration of natural disasters risk to insurance contracts constitutes a significant challenge. All these aspects promote the sustainability of urban and rural regions.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Wildfires monitoring, simulation, modelling, prevention and management. Socioeconomic impacts.
  • Floods monitoring, simulation, modelling, prevention and management. Socioeconomic impacts.
  • Extreme temperature monitoring and modelling. Urban heat island.
  • Drought monitoring, modelling and projection. Food security.
  • Storm monitoring, modelling. Socioeconomic impacts.
  • Construction efficiency against earthquakes.
  • Early warning systems.
  • Integration of geographic information systems, remote sensing and artificial intelligence in the field of natural disasters.
  • Contribution of unmanned aerial vehicles to natural disasters monitoring and management.
  • Climate change and natural disasters.

We look forward to receiving your contributions. 

Dr. Stavros Sakellariou
Dr. Lampros Vasiliades
Dr. Olga Christopoulou
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. Sustainability 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 2400 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.

Keywords

  • natural disasters
  • climate change
  • wildfires
  • floods
  • droughts
  • extreme temperature
  • storms
  • earthquakes
  • early warning systems
  • socioeconomic analysis
  • UAVs

Published Papers (4 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

19 pages, 10352 KiB  
Article
Preliminary Risk Assessment of Geological Disasters in Qinglong Gorge Scenic Area of Taihang Mountain with GIS Based on Analytic Hierarchy Process and Logistic Regression Model
by Ruixia Ma, Yan Lyu, Tianbao Chen and Qian Zhang
Sustainability 2023, 15(22), 15752; https://doi.org/10.3390/su152215752 - 08 Nov 2023
Cited by 1 | Viewed by 789
Abstract
Qinglong Gorge Scenic Area (QGSA) boasts stunning natural landscapes, characterized by towering peaks and extensive cliffs. Nevertheless, the intricate geological backdrop and distinctive topographical conditions of this area give rise to various geological disasters, posing a substantial safety concern for tourists and presenting [...] Read more.
Qinglong Gorge Scenic Area (QGSA) boasts stunning natural landscapes, characterized by towering peaks and extensive cliffs. Nevertheless, the intricate geological backdrop and distinctive topographical conditions of this area give rise to various geological disasters, posing a substantial safety concern for tourists and presenting ongoing operational and safety management challenges for the scenic area. In light of these challenges, this study placed its focus on the geological disasters within QGSA and sought to assess risks across various scales. The assessment was accomplished through a combination of methods, including field surveys conducted in 2022, remote sensing interpretation, and comprehensive data collection and organization. For the geological disaster risk assessment of the scenic area, this research selected seven key indicators, encompassing terrain factors, geological elements, structural characteristics, and other relevant factors. The assessment utilized a logistic regression model, which yielded satisfactory results with an AUC value of 0.8338. Furthermore, a model was constructed incorporating seven indicators, encompassing factors such as population vulnerability, material susceptibility, and the vulnerability of tourism resources. To assess vulnerability to geological disasters, the Analytic Hierarchy Process (AHP) was employed, resulting in a CR of 0, thus ensuring the reliability of the findings. The outcomes of the risk assessment indicate that the low-risk area covers a substantial expanse of 5.45 km2, representing 53.66% of the total area. The moderate-risk area extends over 3.59 km2, constituting 35.43%, while the high-risk area encompasses 0.72 km2, accounting for 7.14%. Additionally, the very high-risk area encompasses 0.38 km2, making up 3.77% of the total area. Consequently, building upon the findings of the risk assessment, this paper introduces a risk classification and control prevention system. This system provides invaluable insights for disaster prevention and control in mountainous and canyon-type scenic areas. Full article
(This article belongs to the Special Issue Natural Disasters: Modelling, Monitoring, Management and Mitigation Procedures)
►▼ Show Figures

Figure 1

24 pages, 3630 KiB  
Article
Fusion of Remotely-Sensed Fire-Related Indices for Wildfire Prediction through the Contribution of Artificial Intelligence
by Nikolaos Ntinopoulos, Stavros Sakellariou, Olga Christopoulou and Athanasios Sfougaris
Sustainability 2023, 15(15), 11527; https://doi.org/10.3390/su151511527 - 25 Jul 2023
Cited by 3 | Viewed by 1318
Abstract
Wildfires are a natural phenomenon, which nowadays, due to the synergistic effect of increased human intervention and the escalation of climate change, are displaying an ever-increasing intensity and frequency. The underlying mechanisms present increased complexity, with the phenomenon itself being characterized by a [...] Read more.
Wildfires are a natural phenomenon, which nowadays, due to the synergistic effect of increased human intervention and the escalation of climate change, are displaying an ever-increasing intensity and frequency. The underlying mechanisms present increased complexity, with the phenomenon itself being characterized by a significant degree of stochasticity. For the above reasons, machine learning models and neural networks are being implemented. In the current study, two types of neural networks are implemented, namely, Artificial Neural Networks (ANN) and Radial Basis Function Networks (RBF). These neural networks utilize information from the Fire Weather Index (FWI), Fosberg Fire Weather Index (FFWI), Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), aiming to predict ignitions in a region of Greece. All indices have been developed through the Google Earth Engine platform (GEE). In addition, a new index is proposed named “Vegetation-Enhanced FWI” (FWIveg) in order to enhance the FWI with vegetation information from the NDVI. To increase the robustness of the methodology, a genetic algorithm-based approach was used in order to obtain algorithms for the calculation of the new index. Finally, an artificial neural network was implemented in order to predict the Mati wildfire in Attica, Greece (23 July 2018) by applying the new index FWIveg, aiming to assess both the effectiveness of the new index as well as the ability to predict ignition events using neural networks. Results highlight the effectiveness of the two indices in providing joint information for fire prediction through artificial intelligence-based approaches. Full article
(This article belongs to the Special Issue Natural Disasters: Modelling, Monitoring, Management and Mitigation Procedures)
►▼ Show Figures

Figure 1

22 pages, 5019 KiB  
Article
Temperature Prediction Based on STOA-SVR Rolling Adaptive Optimization Model
by Shuaihua Shen, Yanxuan Du, Zhengjie Xu, Xiaoqiang Qin and Jian Chen
Sustainability 2023, 15(14), 11068; https://doi.org/10.3390/su151411068 - 15 Jul 2023
Cited by 2 | Viewed by 1050
Abstract
In this paper, a support vector regression (SVR) adaptive optimization rolling composite model with a sooty tern optimization algorithm (STOA) has been proposed for temperature prediction. Firstly, aiming at the problem that the algorithm tends to fall into the local optimum, the model [...] Read more.
In this paper, a support vector regression (SVR) adaptive optimization rolling composite model with a sooty tern optimization algorithm (STOA) has been proposed for temperature prediction. Firstly, aiming at the problem that the algorithm tends to fall into the local optimum, the model introduces an adaptive Gauss–Cauchy mutation operator to effectively increase the population diversity and search space and uses the improved algorithm to optimize the key parameters of the SVR model, so that the SVR model can mine the linear and nonlinear information in the data well. Secondly, the rolling prediction is integrated into the SVR prediction model, and the real-time update and self-regulation principles are used to continuously update the prediction, which greatly improves the prediction accuracy. Finally, the optimized STOA-SVR rolling forecast model is used to predict the final temperature. In this study, the global mean temperature data set from 1880 to 2022 is used for empirical analysis, and a comparative experiment is set up to verify the accuracy of the model. The results show that compared with the seasonal autoregressive integrated moving average (SARIMA), feedforward neural network (FNN) and unoptimized STOA-SVR-LSTM, the prediction performance of the proposed model is better, and the root mean square error is reduced by 6.33–29.62%. The mean relative error is reduced by 2.74–47.27%; the goodness of fit increases by 4.67–19.94%. Finally, the global mean temperature is predicted to increase by about 0.4976 °C in the next 20 years, with an increase rate of 3.43%. The model proposed in this paper not only has a good prediction accuracy, but also can provide an effective reference for the development and formulation of meteorological policies in the future. Full article
(This article belongs to the Special Issue Natural Disasters: Modelling, Monitoring, Management and Mitigation Procedures)
►▼ Show Figures

Figure 1

11 pages, 1879 KiB  
Article
Play to Learn: A Game to Improve Seismic-Risk Perception
by Maria Grazia Filomena, Bruno Pace, Massimo De Acetis, Antonio Aquino, Massimo Crescimbene, Marina Pace and Francesca Romana Alparone
Sustainability 2023, 15(5), 4639; https://doi.org/10.3390/su15054639 - 06 Mar 2023
Viewed by 1458
Abstract
A board game designed by psychologists and geologists to improve seismic-risk perception is presented. In a within-subjects repeated-measure study, 64 Italian high-school students rated their perception of seismic risk in relation to the hazard, vulnerability and exposure of the area in which they [...] Read more.
A board game designed by psychologists and geologists to improve seismic-risk perception is presented. In a within-subjects repeated-measure study, 64 Italian high-school students rated their perception of seismic risk in relation to the hazard, vulnerability and exposure of the area in which they lived, before and after the game. A repeated-measures analysis of variance (ANOVA), which considered perception of seismic risk as the dependent variable and time as the independent variable, revealed that the board game affected the dependent variable, particularly the perception of hazard and vulnerability. The results confirm the effectiveness of the game in changing participants’ seismic risk perception, properly because the game was built with consideration of the variables that make up seismic risk. The board game proved to be an effective and fun educational tool to be used in future earthquake risk prevention programs. Full article
(This article belongs to the Special Issue Natural Disasters: Modelling, Monitoring, Management and Mitigation Procedures)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop