Abstract
Landslide is a chronic problem that causes severe geographical hazard due to development activities and exploitation of the hilly region and it occurs due to heavy and prolongs rain flow in the mountainous area. Initially, a total of 726 locations were identified at devikulam taluk, Idukki district (India). These landslide potential points utilised to construct a spatial database. Then, the geo spatial database is then split randomly into 70% for training the models and 30% for the model validation. This work considers Seven landslide triggering factors for landslide susceptibility mapping. The susceptibility maps were verified using various evaluation metrics. The metrics are sensitivity, specificity, accuracy, precision, Recall, Matthews correlation efficient (MCE), Area Under the Curve (AUC), Kappa statistics, Mean Absolute Error (MAE), Mean Square Error (MSE).The proposed work with 5 advanced machine learning approaches assess the landslide vulnerability.It includes Logistic Regression (LR), K Nearest Neighbor (KNN), Decision tree classifier, Linear Discriminant Analysis (LDA) and Gaussian Naïve Bayes modelling and comparing their performance for the spatial forecast of landslide possibilities in the Devikulam taluk. In experimental results, Decision tree classifier performs the most reliable performance with an overall accuracy rate of 99.21%.
Speeding up Spatial Database Query Execution using GPUs