Predicting Landslide Using Machine Learning Techniques

Abstract

In mountainous areas prone to landslides, it’s crucial to map out where these hazardous events are likely to occur to mitigate risks effectively. This study focuses employing an integrated approach to assess landslide susceptibility using Random Forest (RF), Stacking, Vote, AdaBoostM1, and Bagging. 13 factors influencing landslide occurrence are identified for modeling purposes. To evaluate and compare the models’ performance, multiple statistical methods are employed. The analysis highlights the effectiveness of employing machine learning models, Random Forest (RF), Stacking, Bagging, and Vote methods. The results demonstrate the efficiency of the models in accurately predicting landslide susceptibility. The study suggests that similar hybrid models can be effectively utilized in other sensitive regions with comparable geo-environmental conditions for landslide susceptibility studies. By integrating various techniques and leveraging ensemble algorithms, these models offer improved accuracy and reliability in assessing landslide hazards. This comprehensive approach provides valuable insights for disaster management and risk reduction efforts in landslideprone areas worldwide.