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• W3203594590 abstract "Among the natural hazards that affect humans, landslides play a key role in the devastation it creates and the economic loss it produces. Landslides mostly occur in hilly regions with unstable slopes. It is a complex nonlinear natural dynamical system with built-in uncertainty. The Evolution of landslides is influenced by multiple parameters, including tectonic, rainfall, water table fluctuation, soil condition, nature of the slope, vegetation availability, undercutting, and human activities in the region. Slow-moving landslides cause huge damage in terms of economy and ravaging facilities across their travel path, resulting in huge losses to human life. However, the quantification of fatalities caused by landslides is always underestimated and mainly incomplete; thus, the estimation of landslide risk is rather ambitious. Moreover, there is a growing demand for a detailed, and accurate estimation of landslides and their damage. It is also important to provide hazard mapping that shows the real vulnerability in an area, which will facilitate planners to plan and act. Quantification and estimation of landslide hazards is an hour in many countries where the quantum and frequency of landslides occurring in the last few decades are of major concern. Several methods have been studied by researchers. One such technique employed is machine learning, which is suitable for handling a large amount of data to predict the occurrence of landslides using various attributes. It is found in many studies that the critical role in landslide initiation is not only played by rainfall and earthquakes but also by intrinsic factors such as slope steepness, soil properties, lithology, etc. This work focuses on reviewing recent work done on landslide mapping, rainfall-induced landslides, and predictive methods of machine learning algorithms to map hazard vulnerability in tandem with GIS techniques." @default.
• W3203594590 created "2021-10-11" @default.
• W3203594590 creator A5032434434 @default.
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• W3203594590 date "2022-01-01" @default.
• W3203594590 modified "2023-10-16" @default.
• W3203594590 title "Soft computing applications in rainfall-induced landslide analysis and protection—Recent trends, techniques, and opportunities" @default.
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• W3203594590 doi "https://doi.org/10.1016/b978-0-323-89861-4.00036-1" @default.
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