Rainfall Induced Landslide Detection in Study Area using Machine Learning Model

In the present era of computing, communication and Technology, natural disaster can be controlled in an efficient manner. The natural disaster in hill sate is common. It can be mitigated using machine learning techniques. In this manuscript out objective is proposed a machine learning model which focus on rainfall induced landslide prediction in uttarakhand state districts using benchmark dataset. There is a good correlation between landslide and antecedent rain fall. The antecedent rain fall supports the machine learning model for better accuracy and correctness. The machine learning model with optimal performance metrics provides the prior information about the level rainfall and its impact level on landslide in a study area of the focused state. The results show that Random Forest model outperforms the linear model, SVR model, and neural network model respectively. The key performance indicators i.e. mean absolute error(MAE), and root mean square error(RMSE) are improved by a factor of 79.05%, and 83.34% respectively. The key performance indicators evaluated and analyzed against state of art methodologies i.e. Random Forest model outperforms the linear model, SVR model, and neural network