K.C. Lalmalsawmzauva – författare

This book explores state-of-art techniques based on open-source software and statistical programming and modelling in modern geospatial applications, specifically focusing on recent trends in data mining techniques and robust modelling in Geomorphological, Hydrological, Bio-physical and Social activities. The book is organized into physical, mountainous, coastal, riverine, forest, urban and biological activities, with each chapter providing a review of the current knowledge in the focus area, and evaluating where future efforts should be directed. The text compiles a collection of recent developments and rigorous applications of Geospatial computational intelligence (e.g., artificial neural network, spatial interpolation, physical and environmental modelling and machine learning algorithms etc) in geomorphic processes from a team of expert contributors. The authors address the wide range of challenges and uncertainties in the study of earth system dynamics due to climate change, and complex anthropogenic interferences where spatial modelling may be applied in the risk assessment of vulnerable geomorphological landscapes. The book will act as a guide to find recent advancements in geospatial artificial intelligence techniques and its application to natural and social hazards. This information will be helpful for students, researchers, policy makers, environmentalists, planners involved in natural hazard and disaster management, NGOs, and government organizations.

This book explores state-of-art techniques based on open-source software and statistical programming and modelling in modern geospatial applications, specifically focusing on recent trends in data mining techniques and robust modelling in Geomorphological, Hydrological, Bio-physical and Social activities. The book is organized into physical, mountainous, coastal, riverine, forest, urban and biological activities, with each chapter providing a review of the current knowledge in the focus area, and evaluating where future efforts should be directed. The text compiles a collection of recent developments and rigorous applications of Geospatial computational intelligence (e.g., artificial neural network, spatial interpolation, physical and environmental modelling and machine learning algorithms etc) in geomorphic processes from a team of expert contributors. The authors address the wide range of challenges and uncertainties in the study of earth system dynamics due to climate change, and complex anthropogenic interferences where spatial modelling may be applied in the risk assessment of vulnerable geomorphological landscapes. The book will act as a guide to find recent advancements in geospatial artificial intelligence techniques and its application to natural and social hazards. This information will be helpful for students, researchers, policy makers, environmentalists, planners involved in natural hazard and disaster management, NGOs, and government organizations.

This book explores state-of-art techniques based on open-source software and statistical programming and modelling in modern geospatial applications, specifically focusing on recent trends in data mining techniques and robust modelling in Geomorphological, Hydrological, Bio-physical and Social activities. The book is organized into physical, mountainous, coastal, riverine, forest, urban and biological activities, with each chapter providing a review of the current knowledge in the focus area, and evaluating where future efforts should be directed. The text compiles a collection of recent developments and rigorous applications of Geospatial computational intelligence (e.g., artificial neural network, spatial interpolation, physical and environmental modelling and machine learning algorithms etc) in geomorphic processes from a team of expert contributors. The authors address the wide range of challenges and uncertainties in the study of earth system dynamics due to climate change, and complex anthropogenic interferences where spatial modelling may be applied in the risk assessment of vulnerable geomorphological landscapes. The book will act as a guide to find recent advancements in geospatial artificial intelligence techniques and its application to natural and social hazards. This information will be helpful for students, researchers, policy makers, environmentalists, planners involved in natural hazard and disaster management, NGOs, and government organizations.

This book explores a platform for insightful discussions and scientific discourse on various aspects of landslides and their risk management, with insights focused on the Himalayan states at a sub-regional level. A fundamental reference is thus provided for further research not only in landslides but also in associated natural hazards. The book is divided into four major parts, starting from conceptual models in approaching landslide risk, followed by evaluation of risk, management of landslide risk, end-to-end solutions for landslide risk assessment, and a synopsis. Included are topics such as the nature of landslide hazard impact, scale dependency in landslide hazard and risk analysis, and systematic procedures of landslide hazard mapping for risk assessment using spatial prediction models. The book also covers such important aspects as the response of the insurance industry to landslide risk and the role of administrative bodies in landslide risk assessment and sustainable management. Readers gain ample knowledge through case studies regarding the types of landslides in the Himalayan region, which will help them in their future research hypotheses.