Soil Degradation, Desertification and Restoration
Application of Geospatial Technology and Modeling
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Beschreibung
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.
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.
Delves into cutting-edge techniques based on open-source software and R statistical programming Discusses the global deterioration and depletion of soils Provides an overview of soil erosion, soil degradation, management and conservation measures for sustainable agriculture For a text-to-speech audio version of the Extended Preface to this book, please click this link: sn.pub/n2pjlh
Autor*in
Subodh Chandra Pal
Themen in »Soil Degradation, Desertification and Restoration«
Geospatial, geostatistical techniques and its application Land degradation and desertification Modelling in modern artificial intelligence techniques Geospatial technology approach to data mining techniques Soil degradation induced land degradation and desertification Climate change Geospatial intelligence techniques GIS and statistical models