Development of landslide susceptibility mapping with a multi-variance statistical method approach in Kepahiang Indonesia

Abstract

Landslides are an example of severe natural disasters that occur worldwide and generate many harmful effects that can affect the stability and development of society. A better-quality susceptibility mapping technique for the landslide risk is crucial for mitigating landslides. However, the use of assemblages of multivariate statistical methods is still uncommon in Indonesia, particularly in the Kepahiang Regency of Bengkulu Province. Therefore, the objective of this study was to provide an improved framework for creating landslide susceptibility map (LSM) using multivariate statistical methods, i.e., the analytical hierarchy process (AHP) method, the simple additive weighting (SAW) method and the frequency ratio (FR) method. In this study, we established a landslide inventory considering 15 causative factors using the area under the curve (AUC) validation method and another evaluation technique. The performance of each causative factor was evaluated using multicollinearity and Pearson correlation analysis with regression-based ranking. The LSM results showed that the most susceptible areas were located in the districts of Kabawetan, Kepahiang, and Tebat Karai. The high landslide risk in these areas could be attributed to the slope conditions in mountainous regions, which are characterized by high annual rainfall and seismic activity. The AUC training values of the AHP, SAW, and FR methods were 0.866, 0.838, and 0.812, respectively. Then, on the validation dataset, the AHP method yielded the highest AUC value (0.863), followed by the SAW (0.833) and FR (0.807) methods. Moreover, the AHP method provided a higher accuracy value, which suggests that the AHP method is more suitable than the other methods. Therefore, our research indicated that all algorithm methods generate a positive impact and greatly improve landslide susceptibility evaluation, especially for the preparation of landslide damage assessments in this study area. Finally, the method proposed in this study could improve the feasibility of LSM and provide support for Indonesian government decision-makers in arranging hazard mitigation measures in the Kepahiang Regency, Indonesia.