LANDSLIDE PREDICTION OF KERALA USING MACHINE LEARNING

Abstract

Kerala is known as “God’s own country” for its scenic beauty and unique fea tures. Gorgeous landscapes and breathtaking backwaters have always been a blessing for Kerala. Hold of the Western Ghats and Arabian sea favors Kerala for providing torrential rain. But for the past few years, the scenario changed. In August 2018, a low-pressure system around the start of the month was followed by a monsoon depression several days later, resulting in a protracted period of exceptionally heavy rainfall in Kerala, which causes great lose of life and the estimated value of the infrastructure and buildings at $200 billion USD have been washed out. Kerala is experiencing ferocious rain as the ef fect of different anthropogenic and natural changes; also, the frequency of landslides has increased as a reflection of these. Prediction of those will help in hand for prevention. This study focuses on developing a model for the prediction of landslides us ing Logistic Regression (LR), k-Nearest Neighbour (kNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Support vector Machine (SVM); along with deep neural network algorithms like Artificial Neural Network (ANN) and Convolutional Neu ral Network (CNN) was used, with training (70%), validation (15%), and testing (15%) datasets. Twenty-three factors were considered for the study, but five were eliminated after the multicollinearity test. Even though every model is giving satisfactory results, RF and CNN is giving exceptionally greater results of 0.95 and 0.94 for ROC-AUC and PR-AUC respectively. Not to mention the remarkable result of SVM model with 0.94 (ROC-AUC). Different accuracy checks used were ROC-AUC, PR-AUC, precision, ac curacy score, F1-score, and log loss.