Basanta Raj Adhikari – författare

This book presents the select proceedings of the Virtual Conference on Disaster Risk Reduction (VCDRR 2021).  It emphasizes on the role of civil engineering for a disaster resilient society. It presents latest research in geohazards and their mitigation. Various topics covered in this book are land use, ground response, liquefaction, and disaster mitigation techniques. This book is a comprehensive volume on disaster risk reduction (DRR) and its management for a sustainable built environment. This book will be useful for the students, researchers, policy makers and professionals working in the area of civil engineering, especially disaster management.

This book presents the select proceedings of the Virtual Conference on Disaster Risk Reduction (VCDRR 2021).  It emphasizes on the role of civil engineering for a disaster resilient society. It presents latest research in geohazards and their mitigation. Various topics covered in this book are land use, ground response, liquefaction, and disaster mitigation techniques. This book is a comprehensive volume on disaster risk reduction (DRR) and its management for a sustainable built environment. This book will be useful for the students, researchers, policy makers and professionals working in the area of civil engineering, especially disaster management.  

This book presents the select proceedings of the Virtual Conference on Disaster Risk Reduction (VCDRR 2021). This book will be useful for the students, researchers, policy makers and professionals working in the area of civil engineering, especially disaster management.

This book discusses various relevant topics such as Disaster resilience and Infrastructure, Risk reduction and structural measures, Evidence based approach for DRR Case studies, Numerical modelling and Constructions methods, Prevention Methods and Safety Engineering, Cross cutting issue in DRR and Infrastructure etc.

This book discusses various relevant topics such as Disaster resilience and Infrastructure, Risk reduction and structural measures, Evidence based approach for DRR Case studies, Numerical modelling and Constructions methods, Prevention Methods and Safety Engineering, Cross cutting issue in DRR and Infrastructure etc.

This book explores the use of advanced geospatial techniques in geomorphic hazards modelling and risk reduction. It also compares the accuracy of traditional statistical methods and advanced machine learning methods and addresses the different ways to reduce the impact of geomorphic hazards.In recent years with the development of human infrastructures, geomorphic hazards are gradually increasing, which include landslides, flood and soil erosion, among others. They cause huge loss of human property and lives. Especially in mountainous, coastal, arid and semi-arid regions, these natural hazards are the main barriers for economic development. Furthermore, human pressure and specific human actions such as deforestation, inappropriate land use and farming have increased the danger of natural disasters and degraded the natural environment, making it more difficult for environmental planners and policymakers to develop appropriate long-term sustainability plans. The most challenging task is to develop a sophisticated approach for continuous inspection and resolution of environmental problems for researchers and scientists. However, in the past several decades, geospatial technology has undergone dramatic advances, opening up new opportunities for handling environmental challenges in a more comprehensive manner.With the help of geographic information system (GIS) tools, high and moderate resolution remote sensing information, such as visible imaging, synthetic aperture radar, global navigation satellite systems, light detection and ranging, Quickbird, Worldview 3, LiDAR, SPOT 5, Google Earth Engine and others deliver state-of-the-art investigations in the identification of multiple natural hazards. For a thorough examination, advanced computer approaches focusing on cutting-edge data processing, machine learning and deep learning may be employed. To detect and manage various geomorphic hazards and their impact, several models with a specific emphasis on natural resources and the environment may be created.

This book explores the use of advanced geospatial techniques in geomorphic hazards modelling and risk reduction. It also compares the accuracy of traditional statistical methods and advanced machine learning methods and addresses the different ways to reduce the impact of geomorphic hazards.In recent years with the development of human infrastructures, geomorphic hazards are gradually increasing, which include landslides, flood and soil erosion, among others. They cause huge loss of human property and lives. Especially in mountainous, coastal, arid and semi-arid regions, these natural hazards are the main barriers for economic development. Furthermore, human pressure and specific human actions such as deforestation, inappropriate land use and farming have increased the danger of natural disasters and degraded the natural environment, making it more difficult for environmental planners and policymakers to develop appropriate long-term sustainability plans. The most challenging task is to develop a sophisticated approach for continuous inspection and resolution of environmental problems for researchers and scientists. However, in the past several decades, geospatial technology has undergone dramatic advances, opening up new opportunities for handling environmental challenges in a more comprehensive manner.With the help of geographic information system (GIS) tools, high and moderate resolution remote sensing information, such as visible imaging, synthetic aperture radar, global navigation satellite systems, light detection and ranging, Quickbird, Worldview 3, LiDAR, SPOT 5, Google Earth Engine and others deliver state-of-the-art investigations in the identification of multiple natural hazards. For a thorough examination, advanced computer approaches focusing on cutting-edge data processing, machine learning and deep learning may be employed. To detect and manage various geomorphic hazards and their impact, several models with a specific emphasis on natural resources and the environment may be created.