Computers in Earth and Environmental Sciences

Artificial Intelligence and Advanced Technologies in Hazards and Risk Management

Hamid Reza Pourghasemi

• Publisher: Elsevier
• Year: 2021
• Print ISBN: 9780323898614
• eBook ISBN: 9780323886154
• Topic: Physical Sciences
• Subtopic: Business Intelligence

Computers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Management addresses the need for a comprehensive book that focuses on multi-hazard assessments, natural and manmade hazards, and risk management using new methods and technologies that employ GIS, artificial intelligence, spatial modeling, machine learning tools and meta-heuristic techniques. The book is clearly organized into four parts that cover natural hazards, environmental hazards, advanced tools and technologies in risk management, and future challenges in computer applications to hazards and risk management. Researchers and professionals in Earth and Environmental Science who require the latest technologies and advances in hazards, remote sensing, geosciences, spatial modeling and machine learning will find this book to be an invaluable source of information on the latest tools and technologies available. - Covers advanced tools and technologies in risk management of hazards in both the Earth and Environmental Sciences - Details the benefits and applications of various technologies to assist researchers in choosing the most appropriate techniques for purpose - Expansively covers specific future challenges in the use of computers in Earth and Environmental Science - Includes case studies that detail the applications of the discussed technologies down to individual hazards

Table of contents

• Title of Book
• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• Contributors
• Acknowledgments
• Chapter 1 Predicting dissolved oxygen concentration in river using new advanced machines learning: Long-short term memory (LSTM) deep learning
• Chapter 2 Fractal analysis of valley sections in geological formations of arid areas
• Chapter 3 A data-driven approach for estimating contaminants in natural water
• Chapter 4 Application of analytical hierarchy process (AHP) in landslide susceptibility mapping for Qazvin province, N Iran
• Chapter 5 Assessment of machine learning algorithms in land use classification
• Chapter 6 Evaluation of land use change predictions using CA-Markov model and management scenarios
• Chapter 7 Topographical features and soil erosion processes
• Chapter 8 Mapping the NDVI and monitoring of its changes using Google Earth Engine and Sentinel-2 images
• Chapter 9 Spatiotemporal urban sprawl and land resource assessment using Google Earth Engine platform in Lahore district, Pakistan
• Chapter 10 Using OWA-AHP method to predict landslide-prone areas
• Chapter 11 Multiscale drought hazard assessment in the Philippines
• Chapter 12 Selection of the best pixel-based algorithm for land cover mapping in Zagros forests of Iran using Sentinel-2A data: A case study in Khuzestan province
• Chapter 13 Identify the important driving forces on gully erosion, Chaharmahal and Bakhtiari province, Iran
• Chapter 14 Analysis of social resilience of villagers in the face of drought using LPCIEA indicator case study: Downstream of Dorodzan dam
• Chapter 15 Spatial and seasonal modeling of the land surface temperature using random forest
• Chapter 16 Municipal landfill site selection and environmental impacts assessment using spatial multicriteria decision analysis: A case study
• Chapter 17 Predictive habitat suitability models for Teucrium polium L. using boosted regression trees
• Chapter 18 Ecoengineering practices for soil degradation protection of vulnerable hill slopes
• Chapter 19 Soft computing applications in rainfall-induced landslide analysis and protection—Recent trends, techniques, and opportunities
• Chapter 20 Remote sensing and machine learning techniques to monitor fluvial corridor evolution: The Aras River between Iran and Azerbaijan
• Chapter 21 Studies on plant selection framework for soil bioengineering application
• Chapter 22 IoT applications in landslide prediction and abatement—Trends, opportunities, and challenges
• Chapter 23 Application of WEPP model for runoff and sediment yield simulation from ungauged watershed in Shivalik foot-hills
• Chapter 24 Parameter estimation of a new four-parameter Muskingum flood routing model
• Chapter 25 Predicting areas affected by forest fire based on a machine learning algorithm
• Chapter 26 Pest-infected oak trees identify using remote sensing-based classification algorithms
• Chapter 27 The COVID-19 crisis and its consequences for global warming and climate change
• Chapter 28 Earthquake ionospheric and atmospheric anomalies from GNSS TEC and other satellites
• Chapter 29 Landslide spatial modeling using a bivariate statistical method in Kermanshah Province, Iran
• Chapter 30 Normalized difference vegetation index analysis of forest cover change detection in Paro Dzongkhag, Bhutan
• Chapter 31 Rate of penetration prediction in drilling wells from the Hassi Messaoud oil field (SE Algeria): Use of artificial intelligence techniques and environmental implications
• Chapter 32 Soil erodibility and its influential factors in the Middle East
• Chapter 33 Non-carcinogenic health risk assessment of fluoride in groundwater of the River Yamuna flood plain, Delhi, India
• Chapter 34 Digital soil mapping of organic carbon at two depths in loess hilly region of Northern Iran
• Chapter 35 Hydrochemistry and geogenic pollution assessment of groundwater in Akşehir (Konya/Turkey) using GIS
• Chapter 36 Comparison of the frequency ratio, index of entropy, and artificial neural networks methods for landslide susceptibility mapping: A case study in Pınarbaşı/Kastamonu (North of Turkey)
• Chapter 37 Remote sensing technology for postdisaster building damage assessment
• Chapter 38 Doing more with less: A comparative assessment between morphometric indices and machine learning models for automated gully pattern extraction (A case study: Dashtiari region, Sistan and Baluchestan Province)
• Chapter 39 Identification of land subsidence prone areas and their mapping using machine learning algorithms
• Chapter 40 Monitoring of spatiotemporal changes of soil salinity and alkalinity in eastern and central parts of Iran
• Chapter 41 Kernel-based granulometry of textural pattern measures on satellite imageries for fine-grain sparse woodlands mapping
• Chapter 42 Badland erosion mapping and effective factors on its occurrence using random forest model
• Chapter 43 Application of machine learning algorithms in hydrology
• Chapter 44 Digital soil mapping of soil bulk density in loess derived-soils with complex topography
• Chapter 45 Landslide susceptibility mapping along the Thimphu-Phuentsholing highway using machine learning
• Chapter 46 Drought assessment using the standardized precipitation index (SPI) in GIS environment in Greece
• Chapter 47 COVID-19: An analysis on official reports in Iran and the world along with some comparisons to other hazards
• Chapter 48 Multihazard risk analysis and governance across a provincial capital in northern Iran
• Chapter 49 Distribution patterns in plants: Mapping and priorities for plant conservation
• Chapter 50 Assessing agriculture land-use change using remote sensing data in the Gilan Province, Iran
• Index