Keywords:
Natural disasters.
;
Geographic information systems.
;
Geomorphology.
;
Machine learning.
;
Artificial intelligence.
Description / Table of Contents:
Landslide Susceptibility Assessment Based on Machine Learning Techniques -- Measuring landslide susceptibility in Jakholi region of Garhwal Himalaya applying novel ensembles of statistical and machine learning algorithms -- 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 -- An advanced hybrid machine learning technique for assessing the susceptibility to landslides in the Meenachil river basin of Kerala, India -- Novel ensemble of M5P and Deep learning neural network for predicting landslide susceptibility: A cross-validation approach -- Artificial neural network ensemble with General linear model for modeling the Landslide Susceptibility in Mirik region of West Bengal, India -- Modeling gully erosion susceptibility using advanced machine learning method in Pathro River Basin, India -- Quantitative Assessment of Interferometric Synthetic Aperture 2 Radar(INSAR) for Landslide Monitoring and Mitigation -- Assessment of Landslide Vulnerability using Statistical and Machine Learning Methods in Bageshwar District of Uttarakhand, India -- Assessing the shifting of the River Ganga along Malda District of West Bengal, India -- An ensemble of J48 Decision Tree with AdaBoost, and Bagging for flood susceptibility mapping in the Sundarban of West Bengal, India -- Assessment of mouza level flood resilience in lower part of Mayurakshi River basin, Eastern India -- Spatial flashflood modeling in Beas River Basin of Himachal Pradesh, India using GIS-based machine learning algorithms -- Geospatial study of river shifting and erosion deposition phenomenon along a selected stretch of River Damodar, West Bengal, India -- An Evaluation of Hydrological Modeling Using CN Method in Ungauged Barsa River Basin of Pasakha, Bhutan -- The Adoption of Random Forest (RF) and Support Vector Machine (SVM) with Cat Swarm Optimization (CSO) to Predict the Soil Liquefaction.
Type of Medium:
Online Resource
Pages:
1 Online-Ressource(XVII, 325 p. 134 illus., 124 illus. in color.)
Edition:
1st ed. 2024.
ISBN:
9789819977079
Series Statement:
Disaster Risk Reduction, Methods, Approaches and Practices
URL:
https://doi.org/10.1007/978-981-99-7707-9
DOI:
10.1007/978-981-99-7707-9
Language:
English
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