首页|Studies from Wadia Institute of Himalayan Geology Update Current Data on Machine Learning (Landslide Susceptibility Mapping and Sensitivity Analysis Using Vario us Machine Learning Models: a Case Study of Beas Valley, Indian Himalaya)
Studies from Wadia Institute of Himalayan Geology Update Current Data on Machine Learning (Landslide Susceptibility Mapping and Sensitivity Analysis Using Vario us Machine Learning Models: a Case Study of Beas Valley, Indian Himalaya)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting out of Uttarakhand, India, by NewsRx editors, research stated, "Landslide is one of the most destructive hazards in the Upper Beas valley of the Himalayan region of India. Landslide susceptibility mapping is an important and preliminary task in order to prospect the spatial v ariability of landslide prone zones in the area." Financial support for this research came from Council of Scientific & Industrial Research (CSIR) - India. Our news journalists obtained a quote from the research from the Wadia Institute of Himalayan Geology, "As the use of machine learning algorithms has increased the success rate in susceptibility studies, the performance of the four machine learning models, namely Naive Bayes (NB), K-Nearest Neighbor (KNN), Random Fores t (RF) and Extreme Gradient Boosting (XGBoost) were initially tested for landsli de susceptibility mapping in the area. Landslide inventory containing both lands lide and non-landslide data and thirteen landslide conditioning factors were con sidered to train the models. The models were optimized using hyperparameter opti mization and input factors selection based on variable importance. Among the fou r models, Extreme Gradient Boosting (XGBoost), an advanced ensemble-based machin e learning algorithm, demonstrated superior performance (AUC = similar to 0.91) followed by RF, NB and KNN with AUC values of similar to 0.88, similar to 0.87, and similar to 0.82. Therefore, XGboost model was selected for detailed study, i ncluding sensitivity analysis. The results depict that 44% of the total area falls under high and very high susceptible zones. Southward facing sl opes having inclination between 31 degrees-50 degrees located at an elevation of 2001-3000 m in the vicinity of road and drainage network contain most of the la ndslide susceptible zones."
UttarakhandIndiaAsiaCyborgsEmerg ing TechnologiesMachine LearningWadia Institute of Himalayan Geology
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