Computers in Earth and Environmental Sciences

Computers in Earth and Environmental Sciences

Artificial Intelligence and Advanced Technologies in Hazards and Risk Management

1st Edition - September 22, 2021

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  • Editor: Hamid Pourghasemi
  • eBook ISBN: 9780323886154

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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.

Key Features

  • 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


Researchers, postgraduate students, and professionals in Earth and Environmental hazards, including GIS and RS, geomorphology, geosciences, spatial modelling, and machine learning users in different fields. Land use planners, environmental modellers, hydrologists, geographers, cartographers, meteorologists, ecologists, and those in natural resources

Table of Contents

  • 1. Predicting Dissolved Oxygen Concentration in River using New Advanced Machines Learning: Long-Short Term Memory (LSTM) Deep Learning.
    2. Fractal analysis of valley sections in geological formations of arid areas
    3. Data-driven approach for estimating contaminants in natural water
    4. Application of analytical hierarchy process (AHP) in landslide susceptibility mapping for Qazvin province, N Iran
    5. Assessment of machine learning algorithms in land use classification
    6. Evaluation of land use change predictions using CA-Markov model and managerial scenarios
    7. Topographical features and soil erosion processes
    8. Mapping the NDVI and monitoring of its changes using Google Earth Engine and Sentinel-2 images
    9. Spatiotemporal Urban Sprawl and Land Resource Assessment using Google Earth Engine Platform in Lahore District, Pakistan
    10. Using OWA - AHP method to predict landslide-prone areas
    11. Multi-scale drought hazard assessment in the Philippines
    12. Selection of the best pixel-based algorithm for land cover mapping in Zagros forests of Iran using Sentinel-2A satellite image: A case study in Khuzestan province
    13. Identify the important driving forces on gully erosion, Chaharmahal and Bakhtiari province, Iran
    14. Analysis of social resilience of villagers in the face of drought using LPCIEA indicator, Case study: Downstream of Dorodzan dam, Iran
    15. Spatial and seasonal modelling of land surface temperature using Random Forest
    16. Municipal solid waste landfill suitability analysis through spatial multi-criteria decision analysis: a case study
    17. Predictive habitat suitability models for Teucrium polium L. using boosted regression trees
    18. Ecoengineering practices for Soil degradation protection for vulnerable hill slopes
    19. Soft computing applications in rainfall induced landslide analysis and protection – Recent trends, techniques, and opportunities
    20. Remote sensing and machine learning techniques to monitor fluvial corridor evolution: the Aras River between Iran and Azerbaijan
    21. Studies on potential plant selection focusing on soil bioengineering application for land degradation protection
    22. IoT applications in landslide prediction and abatement – Trends, opportunities and challenge
    23. Application of WEPP model for runoff and sediment yield simulation from ungauged watershed in Shivalik foothills
    24. Parameter estimation of a new four-parameter Muskingum flood routing model
    25. Predicting areas affected by forest fire based on machine learning algorithm
    26. Management of pest-infected oak trees using remote sensing-based classification algorithms and GIS data
    27. The COVID-19 Crisis and Its Consequences for Global Warming and Climate Change
    28. Earthquake anomalies for global events from GNSS TEC and other satellites
    29. Landslide spatial modelling using a bivariate statistical method in Kermanshah Province, Iran
    30. Normalized Difference Vegatation Index analysis of Forest Cover Change Detection in Paro Dzongkhag, Bhutan
    31. Rate of penetration prediction in drilling wells from the Hassi Messaoud oil field (SE Algeria): use of artificial intelligence techniques and environmental implications
    32. Soil erodibility and its influential factors in arid and semi-arid regions of the Middle-East
    33. Non-carcinogenic health risk assessment of fluoride in groundwater of the alluvial plains of River Yamuna, Delhi, India
    34. Digital soil mapping of organic carbon at two depths in loess hilly region of Northern Iran
    35. Hydrochemistry and geogenic pollution assessment of groundwater in Akşehir (Konya/Turkey) using GIS
    36. Comparison of the frequency ratio, index of entropy, and artificial neural networks models for landslide susceptibility mapping: A case study in Pınarbaşı/Kastamonu (North of Turkey)
    37. Remote Sensing Technology for Post-Disaster Building Damage Assessment
    38. Doing More with Less: Coupling Morphometric Indices for Automated Gully Pattern Extraction (A Case Study in the Southeast of Iran)
    39. Identification of land subsidence prone areas and its mapping using machine learning algorithms
    40. Monitoring of Spatiotemporal Changes of Soil Salinity and Alkalinity in Eastern and Central Parts of Iran
    41. Fine-grain Sparse Woodlands Mapping, Using Kernel-based Granulometry of Textural Pattern Measures on Satellite Imageries
    42. Badland erosion mapping and effective factors on its occurrence using random forest model
    43. Application of machine learning algorithms in Hydrology
    44. Digital soil mapping of bulk density in loess derived- soils with complex topography
    45. Landslide Susceptibility Mapping along the Thimphu-Phuentsholing Highway using Machine Learning
    46. Drought Assessment using the Standardized Precipitation Index (SPI) in Greece
    47. COVID-19: An overview on official reports in Iran and world along with some comparisons to other hazards
    48. Multi-hazard risk analysis and governance across a provincial capital in northern Iran

Product details

  • No. of pages: 704
  • Language: English
  • Copyright: © Elsevier 2021
  • Published: September 22, 2021
  • Imprint: Elsevier
  • eBook ISBN: 9780323886154

About the Editor

Hamid Pourghasemi

Hamid Reza Pourghasemi is an Associate Professor of Watershed Management Engineering in the College of Agriculture, Shiraz University, Iran. His main research interests are GIS-based spatial modelling using machine learning/data mining techniques in various fields including as landslides, floods, gully erosion, forest fires, land subsidence, species distribution modelling, and groundwater/hydrology. Hamid Reza also works on multi-criteria decision-making methods in natural resources and environmental science. He has published over 150 peer-reviewed papers in high-quality journals and four Edited books, and is an active reviewer for over 65 international journals. Hamid Reza was selected as one of the five young scientists under 40 by The World Academy of Science (TWAS 2019) and was a highly cited researcher in 2019.

Affiliations and Expertise

Associate Professor, Watershed Management Engineering, College of Agriculture, Shiraz University, Shiraz, Fars, Iran

Ratings and Reviews

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  • Prof. K. Thu Sep 30 2021

    Good collections of Advanced Technologies in Hazards and Risk Management

    Congratulations to Prof. Hamid. It is well compile and covers various Advanced Technologies in Hazards and Risk Management along with AI applications. Book has 47 chapters making a big book.