Harnessing the Power of Machine Learning Algorithms for Landslide Susceptibility Prediction

In landslide-prone mountainous regions, accurate susceptibility prediction is crucial to mitigate dangers. Classical and probabilistic approaches have limited prediction capability. Hence, computational methods, specifically machine learning, are being employed to enhance accuracy. Our study aims to predict landslide susceptibility in the Kashmir Himalayas, specifically Muzaffarabad and the Azad Kashmir region. Initially, we established eleven distinctive features for prediction. We trained and tested seven machine learning models, comparing susceptibility predictions in two classes: susceptible and not susceptible. Evaluating classification performance, we achieved test accuracies ranging from 69.30 to 78.71%. The K-Nearest Neighbors (KNN) algorithm outperformed other models, yielding superior accuracy with an optimal k-value for the chosen dataset.