Revised Rainfall Threshold in the Indonesian Landslide Early Warning System
Ragil Andika Yuniawan, Ahmad Rifa’i, Fikri Faris, Andy Subiyantoro, Ratna Satyaningsih, Alidina Nurul Hidayah, Rokhmat Hidayat, Akhyar Mushthofa, Banata Wachid Ridwan, Eka Priangga, Agus Setyo Muntohar, Victor Jetten, C.J. van Westen, Bastian van den Bout, Samuel Jonson Sutanto
Abstract
Landslides are one of the most disastrous natural hazards that frequently occur in Indonesia. In 2017, Balai Sabo developed an Indonesia Landslide Early Warning System (ILEWS) by utilizing a single rainfall threshold for an entire nation, leading to inaccuracy in landslide predictions. The study aimed to improve the accuracy of the system by updating the rainfall threshold. We analyzed 420 landslide events in Java with the 1-day and 3-day effective antecedent rainfall for each landslide event. Rainfall data were obtained from the Global Precipitation Measurement (GPM), which is also used in the ILEWS. We propose four methods to derive the thresholds: the first is the existing threshold applied in the Balai Sabo ILEWS, the second and third use the average and minimum values of rainfall that trigger landslides, respectively, and the fourth uses the minimum value of rainfall that induces major landslides. We used receiver operating characteristic (ROC) analysis to evaluate the predictability of the rainfall thresholds. The fourth method showed the best results compared with the others, and this method provided a good prediction of landslide events with a low error value. The chosen threshold was then applied in the Balai Sabo-ILEWS.