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Full Description
Natural Disasters Under Changing Climate: Modeling Strategies, Predictions, and Management is an essential textbook within the natural disaster prediction domain. It functions as a comprehensive book on natural disasters, and focuses 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 wide range of natural disasters into account, it covers novel approaches in the field of artificial intelligence and remote sensing in detail. It also 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. This textbook delves into applications of novel artificial intelligence approaches, including machine-learning and deep-learning algorithms and new remote sensing platforms and techniques. It presents the scientific frameworks for spatial prediction of a wide suite of natural disasters with a focus on specific triggers and processes. 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. This textbook is a critical resource for upper-level undergraduate students in earth and environmental sciences, specifically those studying or researching physical geography, environmental sciences, geospatial and geohazard modeling, and integrated watershed management. It is also useful for professionals in the field of environmental science, natural disasters, climate change, and sustainability. This textbook contains study questions and case studies as additional resources for students and instructors.
Contents
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
