Comparative Analysis of Machine Learning Algorithms on Landslide

Landslide is one of the continuous geological disorders during rainy season, which make property damage and economic losses in all part of the world. In worldwide natural disorders landslide take the responsible is 17%. The frequent landslide occurrence has been increased by global climate change which causes losses and damages are increased. Therefore, automatic and accurate prediction of landslide occurrence is important to reduce the damages and losses of property. Since a lot studies have been carried out on landslides and their reduction, this field has seen a lot of positive progress. Landslide incidences are taken as dataset along with associated triggers. We apply Decision Tree (DT) and Random Forest Classifier (RF) and through our experimental evaluations find the suitable algorithm for the proposed work.