New
Natural Disasters Under Changing Climate,
Edition 1 Modeling Strategies, Predictions, and ManagementEditors: Edited by Omid Rahmati, Zahra Kalantari, Carla Sofia Ferreira, Bahram Choubin and Rares Halbac-Cotoara-Zamfir
Publication Date:
01 Sep 2026
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Natural Disasters Under Changing Climate: Modeling Strategies, Predictions, and Management is an essential textbook within the natural disaster prediction domain, functioning as a comprehensive book on natural disasters, and focusing on floods, landslides, earthquakes, dust storms, land subsidence, wildfire, sea level rise, drought, snow avalanches, debris flow, desertification, sand dune migration, and heatwaves. In addition to taking a range of natural disasters into account, it covers novel approaches in artificial intelligence and remote sensing and provides an overview of the different concepts of natural disasters perception and how geo-environmental, topo-hydrological, and edaphic variables are connected with their occurrences.
The initial chapters of the book shed light on the main principles and mechanisms of disasters prediction and the application of artificial intelligence algorithms in natural disasters domain. They discuss the applicability of the predictive models in the natural hazards domain and how the understanding of disaster management can happen with the help of disaster susceptibility maps. The book then pivots into landslide susceptibility modeling under climate change and details the use of DInSAR as a powerful tool for studying the effects of earthquakes in various regions. Following that, dust storm frequency and intensity, and how these are impacted by climatic factors, as well as water and land use management, is discussed at length.
Key Features
- Functions as a comprehensive textbook on natural disasters, focusing on floods, landslides, earthquakes, dust storms, land subsidence, wildfire, sea level rise, drought, snow avalanches, debris flow, and desertification
- Provides unique and cutting-edge research on natural disaster prediction
- Explores novel approaches in the field of artificial intelligence and remote sensing
- Presents scientific frameworks for the spatial prediction of a wide suite of natural disaster types, with a focus on their specific triggers and processes
- Offers study questions and case studies for students and instructors
About the author
Edited by Omid Rahmati, Department of Watershed Management, Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research Education and Extension Organization, Iran; Zahra Kalantari, Professor, KTH Royal Institute of Technology, Sweden; Carla Sofia Ferreira, Polytechnic Institute of Coimbra, Portugal; Bahram Choubin, Assistant Professor, West Azarbaijan Agricultural and Natural Resources Research and Education Center, Urmia, Iran and Rares Halbac-Cotoara-Zamfir, Lecturer Prof. Dr. Eng. Rares Halbac-Cotoara-Zamfir Department of Overland Communication Ways, Foundations and Cadastral Survey Politehnica University Timisoara, Romania
1. Natural Disasters Under Climate Change: Challenges and Issues of Modeling, Prediction and Management
2. Spatial Prediction of Flood Hazard Using Data-Mining Models
3. Landslide Susceptibility Modeling under climate change impact: The Role of Machine Learning for Prioritizing Landslide-Chapter
4. Analysis of Earthquake Effects Using DinSAR
5. Mitigation Strategies for Dust Storm with an Interdisciplinary Approach
6. Spatiotemporal Behavior of Landslides Reactivation Using Optic and Radar Satellite Images
7. Spatiotemporal Pattern of Desertification: The Impacts of Land Use and Climate Changes
8. Deep Learning-Based Flood Hazard Assessment
9. Radar-Based Remote Sensing for Land Subsidence Monitoring and Modeling
10. Wildfire Susceptibility Mapping Using Novel Optimized Hybrid Deep Learning Models
11. Vulnerability to Sea Level Rise in Coastal Coupled Social-Ecological Systems
12. Application of MODIS-based Reflectance Spectral Data for Drought Detection and Prediction
13. Snow Avalanche Prediction in Large-Scale Regions Using Deep-Learning and Metaheuristic Algorithms
14. Modeling of Debris Flow Susceptibility Using Artificial Intelligence Approach
15. Detecting Near-Real Time Flood Extent Using Radar-Based Satellite Images
16. Sand Dune Migration Disaster: Monitoring and Protections
17. Dust events and analysis of farmers' resilience measures
18. Heatwave: Mechanisms, Monitoring, and Predictions
2. Spatial Prediction of Flood Hazard Using Data-Mining Models
3. Landslide Susceptibility Modeling under climate change impact: The Role of Machine Learning for Prioritizing Landslide-Chapter
4. Analysis of Earthquake Effects Using DinSAR
5. Mitigation Strategies for Dust Storm with an Interdisciplinary Approach
6. Spatiotemporal Behavior of Landslides Reactivation Using Optic and Radar Satellite Images
7. Spatiotemporal Pattern of Desertification: The Impacts of Land Use and Climate Changes
8. Deep Learning-Based Flood Hazard Assessment
9. Radar-Based Remote Sensing for Land Subsidence Monitoring and Modeling
10. Wildfire Susceptibility Mapping Using Novel Optimized Hybrid Deep Learning Models
11. Vulnerability to Sea Level Rise in Coastal Coupled Social-Ecological Systems
12. Application of MODIS-based Reflectance Spectral Data for Drought Detection and Prediction
13. Snow Avalanche Prediction in Large-Scale Regions Using Deep-Learning and Metaheuristic Algorithms
14. Modeling of Debris Flow Susceptibility Using Artificial Intelligence Approach
15. Detecting Near-Real Time Flood Extent Using Radar-Based Satellite Images
16. Sand Dune Migration Disaster: Monitoring and Protections
17. Dust events and analysis of farmers' resilience measures
18. Heatwave: Mechanisms, Monitoring, and Predictions
ISBN:
9780443338793
Page Count:
400
Retail Price (USD)
:
Students in undergraduate courses on Natural Disasters