Measuring Landslide Susceptibility in Jakholi Region of Garhwal Himalaya Using Landsat Images and Ensembles of Statistical and Machine Learning Algorithms

Landslide susceptibility in the Jakholi region of Garhwal Himalaya was assessed applying novel ensembles of statistical and machine learning algorithms. To begin, landslides were established and a landslide inventory map was prepared. The total locations considered for this study were divided into 70% for the training datasets and 30% for the validation datasets. Following that, a total of 15 landslide conditioning factors were chosen. Furthermore, the certainty factor approach was used for conducting a study of the correlation between conditioning factors as well as landslides. Following that, the CF, CF-SVM, CF-ANN, and CF-RF ensemble models were used for landslide susceptibility modeling and zoning. Finally, the average performance of the four models was evaluated and compared using the receiver operating characteristic (ROC) curve and statistical parameters. For the four models, the area under the curve (AUC) comes out to be greater than 0.81. According to the findings. ROC results indicate that the CF-RF ensemble model gave better performance and the CF model gave comparatively low accuracy. Additionally, this research also showed that an integrated model isn’t always better as compared to a single model. This ensemble analysis can be used as a useful method for land planning and monitoring in the future. It can be successfully utilized for the simulation of other geohazards.