How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions. They are responsible for substantial damages and losses in those areas. The A1 Highway, which is an important road in Algeria, was sometimes constructed in mountainous and/or semi-mountainous areas. Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results. In this research, we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor. To do this, an important section at Ain Bouziane (NE, Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods, namely, random forest (RF), support vector machine (SVM), and boosted regression tree (BRT). First, an inventory map and nine input factors were prepared for landslide susceptibility mapping (LSM) analyses. The three models were constructed to find the most susceptible areas to this phenomenon. The results were assessed by calculating the receiver operating characteristic (ROC) curve, the standard error (Std. error), and the confidence interval (CI) at 95%. The RF model reached the highest predictive accuracy (AUC ​= ​97.2%) comparatively to the other models. The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section. In addition, their application gives an improvement of the accuracy of LSMs near the road corridor. The machine learning models may become an important prediction tool that will identify landslide alleviation actions.