E-Book, Englisch, 325 Seiten, eBook
Sarkar / Saha / Adhikari Geomorphic Risk Reduction Using Geospatial Methods and Tools
1. Auflage 2024
ISBN: 978-981-99-7707-9
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 325 Seiten, eBook
Reihe: Earth and Environmental Science
ISBN: 978-981-99-7707-9
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Weitere Infos & Material
PART 1: GEOMORPHIC HAZARDS AND MACHINE LEARNING TECHNIQUES
Chapter 1: Landslide Susceptibility Assessment Based on Machine Learning Techniques
Jierui Li, Wen He, Lingke Qiu, Wen Zeng, Baofeng Di
Chapter 2: Measuring landslide susceptibility in Jakholi region of Garhwal Himalaya applying novel ensembles of statistical and machine learning algorithms
Sunil Saha, Anik Saha, Raju Sarkar, Kaustuv Mukherjee, Dhruv Bhardwaj
Chapter 3: Landslide Susceptibility Mapping using GIS-based Frequency Ratio, Shannon Entropy, Information Value and Weight-of-Evidence approaches in part of Kullu district, Himachal Pradesh, India
Raju Sarkar, Baboo Chooreshwarsingh Sujeewon, Aman Pawar
Chapter 4: An advanced hybrid machine learning technique for assessing the susceptibility to landslides in the Meenachil river basin of Kerala, India
Anik Saha, Bishnu Roy, Sunil Saha, Ankit Chaudhary, Raju Sarkar
Chapter 5: Novel ensemble of M5P and Deep learning neural network for predicting landslide susceptibility: A cross-validation approach
Anik Saha, Sunil Saha, Ankit Chaudhary, Raju Sarkar
Chapter 6: Artificial neural network ensemble with General linear model for modeling the Landslide Susceptibility in Mirik region of West Bengal, India
Sunil Saha, Anik Saha, Bishnu Roy, Ankit Chaudhary, Raju Sarkar
Chapter 7: Modeling gully erosion susceptibility using advanced machine learning method in Pathro River Basin, India
Amiya Gayen, S.K. Mafizul Haque
PART 2: GEOMORPHIC HAZARDS AND MULTI-TEMPORAL SATELLITE IMAGES
Chapter 8: Quantitative Assessment of Interferometric Synthetic Aperture 2 Radar (INSAR) for Landslide Monitoring and Mitigation
Rachael Lau, Carolina Seguí, Tyler Waterman, Alexander Handwerger, Nathaniel Chaney, Manolis Veveakis
Chapter 9: Assessment of Landslide Vulnerability using Statistical and Machine Learning Methods in Bageshwar District of Uttarakhand, India
Suktara Khatun, Anik Saha, Priyanka Gogoi, Sunil Saha, Raju Sarkar
PART 3: GEOMORPHIC HAZARDS RISK REDUCTION AND MANAGEMENT
Chapter 10: Assessing the shifting of the River Ganga along Malda District of West Bengal, India
Biswajit Roy, Priyanka Gogoi, Sunil Saha
Chapter 11: An ensemble of J48 Decision Tree with AdaBoost, and Bagging for flood susceptibility mapping in the Sundarban of West Bengal, India
Sujata Pal, Anik Saha, Priyanka Gogoi, Sunil Saha
Chapter 12: Assessment of mouza level flood resilience in lower part of Mayurakshi River basin, Eastern India
Gopal Chandra Paul and Sunil Saha
Chapter 13: Spatial flashflood modeling in Beas River Basin of Himachal Pradesh, India using GIS-based machine learning algorithms
Sunil Saha, Anik Saha, Abhishek Agarwal, Ankit Kumar, Raju Sarkar
Chapter 14: Geospatial study of river shifting and erosion deposition phenomenon along a selected stretch of River Damodar, West Bengal, India
Raju Thapa, Raju Sarkar, Srimanta Gupta, Harjeet Kaur, Nasibul Alam
Chapter 15: An Evaluation of Hydrological Modeling Using CN Method in Ungauged Barsa River Basin of Pasakha, Bhutan
Leki Dorji, Raju Sarkar
Chapter 16: The Adoption of Random Forest (RF) and Support Vector Machine (SVM) with Cat Swarm Optimization (CSO) to Predict the Soil Liquefaction
Nerusupalli Dinesh Kumar Reddy, A. K. Gupta, A. K. Sahu




