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Spatial Modeling in GIS and R for Earth and Environmental Sciences

  • Book
  • January 2019
  • Elsevier Science and Technology
  • ID: 4612905

Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions.

The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling.

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

Table of Contents

1. Spatial Analysis of Extreme Rainfall Values Based on Support Vector Machines Optimized by Genetic Algorithms: The Case of Alfeios Basin, Greece 2. Remotely Sensed Spatial and Temporal Variations of Vegetation Indices Subjected to Rainfall Amount and Distribution Properties 3. Numerical Recipes for Landslide Spatial Prediction by Using R-INLA: A Step-By-Step Tutorial 4. An Integrative Approach of Geospatial Multi-Criteria Decision Analysis for Forest Operational Planning 5. Parameters Optimization of KINEROS2 Using Particle Swarm Optimization Algorithm within R Environment for Rainfall-Runoff Simulation 6. Land-Subsidence Spatial Modeling Using Random Forest Data Mining Technique 7. GIS-Based SWARA and its Ensemble by RBF and ICA Data Mining Techniques for Determining Suitability of Existing Schools and Site Selection of New School Buildings 8. Application of SWAT and MCDM Models for Identifying and Ranking the Suitable Sites for Subsurface Dams 9. Habitat Suitability Mapping of Artemisia Aucheri Boiss Based on GLM Model in R 10. Flood-Hazard Assessment Modeling Using Multi-Criteria Analysis and GIS: A Case Study: Ras Gharib Area, Egypt 11. Landslide Susceptibility Survey Using Modelling Methods 12. Prediction of Soil Disturbance Susceptibility Maps of Forest Harvesting Using R and GIS-Based Data Mining Techniques 13. Spatial Modeling of Gully Erosion Using Linear and Quadratic Discriminant Analyses in GIS and R 14. Artificial Neural Networks for Flood Susceptibility Mapping in Data-Scarce Urban Areas 15. Modelling the Spatial Variability of Forest Fire Susceptibility Using Geographical Information Systems (GIS) and Analytical Hierarchy Process (AHP) 16. Prioritization of Flood Inundation of Maharloo Watershed in Iran Using Morphometric Parameters Analysis and TOPSIS MCDM Model 17. A Robust R-M-R (Remote Sensing Spatial Modeling Remote Sensing) Approach for Flood Hazard Assessment 18. Prioritization of Effective Factors on Zataria Multiflora Habitat Suitability and Its Spatial Modeling 19. Prediction of Soil Organic Carbon Using Regression Kriging Model and Remote Sensing Data 20. 3D Reconstruction of Landslides for the Acquisition of Digital Databases and Monitoring Spatio-Temporal Dynamics of Landslides based on GIS Spatial Analysis and UAV Techniques 21. A Comparative Study of Functional Data Analysis and Generalized Linear Model Data Mining Techniques for Landslide Spatial Modelling 22. Regional Groundwater Potential Analysis Using Classification and Regression Trees 23. Comparative Evaluation of Decision-Forest Algorithms in Object-Based Land Use and Land Cover Mapping 24. Statistical Modelling of Landslides: Landslide Susceptibility and Beyond 25. Assessing the Vulnerability of Groundwater to Salinization Using GIS-Based Data Mining Techniques in a Coastal Aquifer 26. A Framework for Multiple Moving Objects Detection in Aerial Videos 27. Modelling Soil Burn Severity Prediction for Planning Measures to Mitigate Post Wildfire Soil Erosion in NW Spain 28. Factors Influencing Regional Scale Wildfire Probability in Iran: An Application of Random Forest and Support Vector Machine 29. Land Use/Land Cover Change Detection and Urban Sprawl Analysis 30. Spatial Modeling of Gully Erosion: A New Ensemble of CART and GLM Data Mining Algorithms 31. Multi-Hazard Exposure Assessment on the Valjevo City Road Network 32. Producing a Spatially Focused Landslide Susceptibility Map Using an Ensemble of Shannon's Entropy and Fractal Dimension (The Ziarat Watershed, Iran) 33. A Conceptual Model on Relationship between Plant Spatial Distribution and Desertification Trend in Rangeland Ecosystems


Hamid Reza Pourghasemi Associate Professor, Watershed Management Engineering, College of Agriculture, Shiraz University, Shiraz, Fars, Iran. 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. Candan Gokceoglu Professor and Chairman, Applied Geology Division, Hacettepe University, Ankara, Turkey. Candan Gokceoglu is Professor and Chairman of the Applied Geology Division at Hacettepe University. He has published more than 175 articles in academic journals and is an Associate Editor of the Elsevier journal Computers and Geosciences.