Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area - Ghasemian, Bahareh; Shahabi, Himan; Shirzadi, Ataollah; Al-Ansari, Nadhir; Jaafari, Abolfazl; Geertsema, Marten; Melesse, Assefa M.; Singh, Sushant K.; Ahmad, Anuar (Frontiers in Environmental Science, 2022)
Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we apply a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF, for a mountainous area near Kamyaran city, Kurdistan province, Iran. We used 118 landslide locations and applied 25 conditioning factors which their predictive usefulness was tested by the Chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, kappa, and area under the receiver operatic characteristic curve (AUC) to check the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map by the hybrid model had the highest goodness-of-fit (AUC=0.953) and higher prediction accuracy (AUC=0.919) compared to other benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world.