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    • Nyhet

    Natural Disasters Under Changing Climate

    Modeling Strategies, Predictions, and Management

    AvOmid Rahmati,Zahra Kalantari

    Häftad, Engelska, 2026

    1 225 kr

    Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

    Beskrivning

    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.

    • 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

    Produktinformation

    • Utgivningsdatum:2026-07-22
    • Mått:191 x 235 x undefined mm
    • Vikt:450 g
    • Format:Häftad
    • Språk:Engelska
    • Antal sidor:752
    • Förlag:Elsevier Science
    • ISBN:9780443338793

    Utforska kategorier

    • Miljöteknik inom Naturvetenskap och teknik
    • Miljövetenskap och miljöpolitik inom Naturvetenskap och teknik

    Mer om författaren

    Dr. Omid Rahmati is a geo-environmental researcher and Assistant Professor at the Agricultural Research, Education, and Extension Organization (AREEO) in Iran. His research focuses on applying machine learning models to natural hazard mitigation and watershed management. He has authored and co-authored over 70 articles in international peer-reviewed journals, as well as several books and book chapters. Dr. Rahmati’s publications have been cited more than 12,500 times (H-index: 56), and he has been recognized as a Highly Cited Researcher. He is ranked among the World’s Top 1% of Scientists by Web of Science (Clarivate, 2021–2022) and listed in Stanford University’s “World’s Top 2% Scientists” from 2021 to 2025. His publications over the past decade reflect a broad and significant influence in his field. Zahra Kalantari has successfully led and carried out interdisciplinary research with focus on understanding of earth and human systems to develop science, technology and innovation solutions to planet’s most pressing environmental challenges associated with the combined effects of changes in climate, land-use and water-use in terrestrial environments.Carla Sofia Ferreira is at Polytechnic Institute of Coimbra, Portugal Bahram Choubin is an Assistant Professor at the West Azarbaijan Agricultural and Natural Resources Research and Education Center in Urmia, Iran. He has been working at this center since 2019. Before that, he was a Postdoctoral Researcher at the University of Tehran from 2018 to 2019. He has published more than 50 articles in JCR-indexed journals and is a scientific reviewer for more than 80 ISI journals. According to Elsevier's World Scientists Rankings, he is among the top 2% of scientists in the world. Choubin's primary research interests encompass hydrometeorology, natural hazard prediction, forecasting in ungauged basins (PUB), advanced machine learning techniques, and cutting-edge radar/remodeling applications in hazard assessment. As a prolific co-author of numerous peer-reviewed research articles and book chapters within these fields, Choubin has been recognized with several prestigious awards for his scientific contributions (e.g., from Iran's National Elites Foundation). Dr. Eng. Rares Halbac-Cotoara-Zamfir is Lecturer and PhD supervisor at Politehnica University of Timisoara, Faculty of Civil Engineering. He has a MSc in Environment Protection and a PhD in Civil Engineering (integrating also elements of environment and sustainable development). In 2016, Dr. Eng. Halbac-Cotoara-Zamfir Rares successfully defended his habilitation thesis “land reclamation and improvement works and sustainable land management in the context of climatic changes”. He spent fifteen years in academia as PhD candidate, Assistant Professor and Lecturer with teaching and transnational project research responsibilities in civil engineering (land reclamation and improvement, sustainable land management, water resources management), sustainable development, environment protection natural hazards, climate change adaptation. Dr. Eng. Rares Halbac-Cotoara-Zamfir was and is currently involved in EEA, Horizon, COST and EUKI projects.

    Innehållsförteckning

    • Natural Disasters Under Climate Change: Challenges and Issues of Modeling, Prediction and ManagementClimate Change and Agricultural Droughts: Understanding the Growing ChallengeDust Storms as a Climate-Driven Natural Disaster: Policy and Challenges in the Middle EastModeling Landslide Susceptibility Under Climate Change: Using Machine Learning in Prioritizing Landslide-Influencing FactorsDeep Learning-Based Modeling of Land Subsidence Derived from PSInSAR Analysis of Radar ImageryEvaluation of Climate Change Impacts on Gully ErosionA Web-based Deep Learning Flood Monitoring and Forecasting Decision Support System with Streamlit Online PlatformVegetation drought in natural resources monitoringDeep Learning-Based Flood Hazard AssessmentVulnerability to Sea Level Rise in Coastal Coupled Social-Ecological SystemsInvestigation of climate change impacts on floods: an overviewFrom Adaptation to Maladaptation: A Cognitive-Conceptual-Assessment Model for Climate Decision-MakingSmart Reforestation for Climate-Resilient Recovery After Wildfire: A Geospatial Approach in the Osoyoos Lake Region, CanadaDust events and analysis of farmers' resilience measuresAnalyzing Urban Heat Island Dynamics: Insights from Machine LearningTowards the application of machine learning techniques for earthquake hazard predictionDesertification and climate changeLand degradation due to climate changeImpacts of climate change on groundwater level decline and land subsidenceSupporting plants in drought conditions using superabsorbent polymers and mycorrhiza fungiSoil Erosion Responses to Diverse Rainfall Events Under Climate ChangeThe effect of drought on the development of gully erosionWater Resources Crisis Management in the Agricultural Sector under Climate ChangeMonitoring Meteorological Drought Using Remote Sensing DataAnalyzing the impact of climate change (drought) on food securitySnow avalanche modeling using SOM and DQL models