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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xiii 8. Acknowledgments - p. xvi 9. Chapter 1. Predicting dissolved oxygen concentration in river using new advanced machines learning: Long-short term memory (LSTM) deep learning - p. 1 10. Chapter 2. Fractal analysis of valley sections in geological formations of arid areas - p. 27 11. Chapter 3. A data-driven approach for estimating contaminants in natural water - p. 37 12. Chapter 4. Application of analytical hierarchy process (AHP) in landslide susceptibility mapping for Qazvin province, N Iran - p. 56 13. Chapter 5. Assessment of machine learning algorithms in land use classification - p. 97 14. Chapter 6. Evaluation of land use change predictions using CA-Markov model and management scenarios - p. 105 15. Chapter 7. Topographical features and soil erosion processes - p. 117 16. Chapter 8. Mapping the NDVI and monitoring of its changes using Google Earth Engine and Sentinel-2 images - p. 132 17. Chapter 9. Spatiotemporal urban sprawl and land resource assessment using Google Earth Engine platform in Lahore district, Pakistan - p. 137 18. Chapter 10. Using OWA-AHP method to predict landslide-prone areas - p. 161 19. Chapter 11. Multiscale drought hazard assessment in the Philippines - p. 169 20. 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 - p. 181 21. Chapter 13. Identify the important driving forces on gully erosion, Chaharmahal and Bakhtiari province, Iran - p. 191 22. Analysis of social resilience of villagers in the face of drought using LPCIEA indicator case study: Downstream of Dorodzan dam - p. 199 23. Chapter 15. Spatial and seasonal modeling of the land surface temperature using random forest - p. 221 24. Chapter 16. Municipal landfill site selection and environmental impacts assessment using spatial multicriteria decision analysis: A case study - p. 235 25. Predictive habitat suitability models for Teucrium polium L. using boosted regression trees - p. 245 26. Chapter 18. Ecoengineering practices for soil degradation protection of vulnerable hill slopes - p. 255 27. Chapter 19. Soft computing applications in rainfall-induced landslide analysis and protection—Recent trends, techniques, and opportunities - p. 280 28. Chapter 20. Remote sensing and machine learning techniques to monitor fluvial corridor evolution: The Aras River between Iran and Azerbaijan - p. 289 29. Chapter 21. Studies on plant selection framework for soil bioengineering application - p. 309 30. Chapter 22. IoT applications in landslide prediction and abatement—Trends, opportunities, and challenges - p. 319 31. Application of WEPP model for runoff and sediment yield simulation from ungauged watershed in Shivalik foot-hills - p. 327 32. Chapter 24. Parameter estimation of a new four-parameter Muskingum flood routing model - p. 337 33. Chapter 25. Predicting areas affected by forest fire based on a machine learning algorithm - p. 351 34. Chapter 26. Pest-infected oak trees identify using remote sensing-based classification algorithms - p. 363 35. Chapter 27. The COVID-19 crisis and its consequences for global warming and climate change - p. 377 36. Chapter 28. Earthquake ionospheric and atmospheric anomalies from GNSS TEC and other satellites - p. 387 37. Chapter 29. Landslide spatial modeling using a bivariate statistical method in Kermanshah Province, Iran - p. 408 38. Chapter 30. Normalized difference vegetation index analysis of forest cover change detection in Paro Dzongkhag, Bhutan - p. 417 39. 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 - p. 427 40. Chapter 32. Soil erodibility and its influential factors in the Middle East - p. 441 41. Chapter 33. Non-carcinogenic health risk assessment of fluoride in groundwater of the River Yamuna flood plain, Delhi, India - p. 455 42. Chapter 34. Digital soil mapping of organic carbon at two depths in loess hilly region of Northern Iran - p. 467 43. Chapter 35. Hydrochemistry and geogenic pollution assessment of groundwater in Akşehir (Konya/Turkey) using GIS - p. 477 44. 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) - p. 494 45. Chapter 37. Remote sensing technology for postdisaster building damage assessment - p. 509 46. 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) - p. 523 47. Chapter 39. Identification of land subsidence prone areas and their mapping using machine learning algorithms - p. 538 48. Chapter 40. Monitoring of spatiotemporal changes of soil salinity and alkalinity in eastern and central parts of Iran - p. 549 49. Chapter 41. Kernel-based granulometry of textural pattern measures on satellite imageries for fine-grain sparse woodlands mapping - p. 563 50. Chapter 42. Badland erosion mapping and effective factors on its occurrence using random forest model - p. 577 51. Chapter 43. Application of machine learning algorithms in hydrology - p. 585 52. Chapter 44. Digital soil mapping of soil bulk density in loess derived-soils with complex topography - p. 593 53. Chapter 45. Landslide susceptibility mapping along the Thimphu-Phuentsholing highway using machine learning - p. 601 54. Chapter 46. Drought assessment using the standardized precipitation index (SPI) in GIS environment in Greece - p. 619 55. Chapter 47. COVID-19: An analysis on official reports in Iran and the world along with some comparisons to other hazards - p. 635 56. Chapter 48. Multihazard risk analysis and governance across a provincial capital in northern Iran - p. 655 57. Chapter 49. Distribution patterns in plants: Mapping and priorities for plant conservation - p. 675 58. Chapter 50. Assessing agriculture land-use change using remote sensing data in the Gilan Province, Iran - p. 687 59. Index - p. 697
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xiii 8. Acknowledgments - p. xvi 9. Chapter 1. Predicting dissolved oxygen concentration in river using new advanced machines learning: Long-short term memory (LSTM) deep learning - p. 1 10. Chapter 2. Fractal analysis of valley sections in geological formations of arid areas - p. 27 11. Chapter 3. A data-driven approach for estimating contaminants in natural water - p. 37 12. Chapter 4. Application of analytical hierarchy process (AHP) in landslide susceptibility mapping for Qazvin province, N Iran - p. 56 13. Chapter 5. Assessment of machine learning algorithms in land use classification - p. 97 14. Chapter 6. Evaluation of land use change predictions using CA-Markov model and management scenarios - p. 105 15. Chapter 7. Topographical features and soil erosion processes - p. 117 16. Chapter 8. Mapping the NDVI and monitoring of its changes using Google Earth Engine and Sentinel-2 images - p. 132 17. Chapter 9. Spatiotemporal urban sprawl and land resource assessment using Google Earth Engine platform in Lahore district, Pakistan - p. 137 18. Chapter 10. Using OWA-AHP method to predict landslide-prone areas - p. 161 19. Chapter 11. Multiscale drought hazard assessment in the Philippines - p. 169 20. 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 - p. 181 21. Chapter 13. Identify the important driving forces on gully erosion, Chaharmahal and Bakhtiari province, Iran - p. 191 22. Analysis of social resilience of villagers in the face of drought using LPCIEA indicator case study: Downstream of Dorodzan dam - p. 199 23. Chapter 15. Spatial and seasonal modeling of the land surface temperature using random forest - p. 221 24. Chapter 16. Municipal landfill site selection and environmental impacts assessment using spatial multicriteria decision analysis: A case study - p. 235 25. Predictive habitat suitability models for Teucrium polium L. using boosted regression trees - p. 245 26. Chapter 18. Ecoengineering practices for soil degradation protection of vulnerable hill slopes - p. 255 27. Chapter 19. Soft computing applications in rainfall-induced landslide analysis and protection—Recent trends, techniques, and opportunities - p. 280 28. Chapter 20. Remote sensing and machine learning techniques to monitor fluvial corridor evolution: The Aras River between Iran and Azerbaijan - p. 289 29. Chapter 21. Studies on plant selection framework for soil bioengineering application - p. 309 30. Chapter 22. IoT applications in landslide prediction and abatement—Trends, opportunities, and challenges - p. 319 31. Application of WEPP model for runoff and sediment yield simulation from ungauged watershed in Shivalik foot-hills - p. 327 32. Chapter 24. Parameter estimation of a new four-parameter Muskingum flood routing model - p. 337 33. Chapter 25. Predicting areas affected by forest fire based on a machine learning algorithm - p. 351 34. Chapter 26. Pest-infected oak trees identify using remote sensing-based classification algorithms - p. 363 35. Chapter 27. The COVID-19 crisis and its consequences for global warming and climate change - p. 377 36. Chapter 28. Earthquake ionospheric and atmospheric anomalies from GNSS TEC and other satellites - p. 387 37. Chapter 29. Landslide spatial modeling using a bivariate statistical method in Kermanshah Province, Iran - p. 408 38. Chapter 30. Normalized difference vegetation index analysis of forest cover change detection in Paro Dzongkhag, Bhutan - p. 417 39. 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 - p. 427 40. Chapter 32. Soil erodibility and its influential factors in the Middle East - p. 441 41. Chapter 33. Non-carcinogenic health risk assessment of fluoride in groundwater of the River Yamuna flood plain, Delhi, India - p. 455 42. Chapter 34. Digital soil mapping of organic carbon at two depths in loess hilly region of Northern Iran - p. 467 43. Chapter 35. Hydrochemistry and geogenic pollution assessment of groundwater in Akşehir (Konya/Turkey) using GIS - p. 477 44. 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) - p. 494 45. Chapter 37. Remote sensing technology for postdisaster building damage assessment - p. 509 46. 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) - p. 523 47. Chapter 39. Identification of land subsidence prone areas and their mapping using machine learning algorithms - p. 538 48. Chapter 40. Monitoring of spatiotemporal changes of soil salinity and alkalinity in eastern and central parts of Iran - p. 549 49. Chapter 41. Kernel-based granulometry of textural pattern measures on satellite imageries for fine-grain sparse woodlands mapping - p. 563 50. Chapter 42. Badland erosion mapping and effective factors on its occurrence using random forest model - p. 577 51. Chapter 43. Application of machine learning algorithms in hydrology - p. 585 52. Chapter 44. Digital soil mapping of soil bulk density in loess derived-soils with complex topography - p. 593 53. Chapter 45. Landslide susceptibility mapping along the Thimphu-Phuentsholing highway using machine learning - p. 601 54. Chapter 46. Drought assessment using the standardized precipitation index (SPI) in GIS environment in Greece - p. 619 55. Chapter 47. COVID-19: An analysis on official reports in Iran and the world along with some comparisons to other hazards - p. 635 56. Chapter 48. Multihazard risk analysis and governance across a provincial capital in northern Iran - p. 655 57. Chapter 49. Distribution patterns in plants: Mapping and priorities for plant conservation - p. 675 58. Chapter 50. Assessing agriculture land-use change using remote sensing data in the Gilan Province, Iran - p. 687 59. Index - p. 697
