Assessing the applicability of rainfall index spatial interpolation in predicting landslide susceptibility: A study in Brunei Darussalam

Abstract

Abstract Rainfall is crucial in meteorology and hydrology, significantly impacting landslide risk assessment. This study focused on evaluating rainfall’s role in determining landslide susceptibility in Brunei Darussalam’s Jalan Kota Batu-Subok and Jalan Jangsak-Tutong regions. Using monthly rainfall data (2008 – 2018) from four weather stations, three spatial interpolation methods inverse distance weighting (IDW), radial basis function (RBF), and global polynomial interpolation (GPI) were assessed. The RBF proved superior in predicting rainfall distribution, evidenced by lower error metrics and higher correlation coefficients. The landslide susceptibility index (LSI) derived from the RBF’s rainfall interpolation showed high accuracy in identifying landslide-prone areas, with success rates between 89 % and 94.3 %, and prediction rates from 85.2 % to 95.9 % across the two areas studied. These findings suggest that the RBF-derived LSI is a reliable tool for landslide risk assessment. However, the LSI’s stability, irrespective of the rainfall data or interpolation method used, indicates that factors like terrain and human activities might have a more significant impact on landslide risks than rainfall alone. This research highlights the importance of considering various factors in landslide risk management and land-use planning, offering valuable insights for policymakers and local authorities.