This book presents a comprehensive geospatial technology approach to data mining, data analysis, modeling, risk assessment, visualization, and management strategies, with a specific focus on various aspects of soil degradation-induced land degradation and desertification. It explores advanced methodologies leveraging open-source software, R statistical programming, Google Earth Engine, and modern artificial intelligence techniques. Particular emphasis is placed on recent advancements in data mining and robust modeling approaches related to extreme events associated with land degradation crises.
The volume brings together recent developments and rigorous applications of geospatial and geostatistical techniques in the context of soil degradation and desertification. It covers a wide range of analytical methods, including the Analytic Hierarchy Process (AHP), Multi-Criteria Decision Making (MCDM), Logistic Regression (LR), Evidential Belief Function (EBF), as well as supervised and unsupervised classification algorithms. Additionally, it highlights the use of Artificial Neural Networks (ANN), Machine Learning Algorithms (MLA), Support Vector Machines (SVM), Fuzzy Logic (FZ), Radial Basis Function (RBF) networks, General Regression Neural Networks (GRNN), Probabilistic Neural Networks (PNN), Mixture Density Networks (MDN), Self-Organizing Maps (SOM), and various hybrid computational techniques.
