Flood Risk Clustering Based on SARIMA Rainfall Prediction and Regional Mapping in Central Java - Maulidiyah, Wildatul; Rahmasari, Hazelita Dwi; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul (Jurusan Matematika FMIPA UNM, 2025)
High and spatially uneven rainfall is a major contributing factor to flooding in tropical regions such as Indonesia, including Central Java Province. This study aims to classify regions based on rainfall patterns using the Dynamic Time Warping (DTW) method and hierarchical clustering, followed by rainfall forecasting for each cluster using the SARIMA model. The dataset comprises monthly rainfall records from 2017 to 2023 across 35 regencies and cities in Central Java. The clustering process identified three distinct groups with low, medium, and high rainfall intensity. Evaluation results indicated that the single linkage models for each cluster were SARIMA(0,0,2)(0,1,0)[12] with a MAPE of 27% (Cluster 1), SARIMA(0,1,2)(0,1,1)[12] with a MAPE of 9.4% (Cluster 2), and SARIMA(1,0,0)(1,1,0)[12] with a MAPE of 9.97% (Cluster 3). These findings provide a robust spatio-temporal basis for supporting flood risk mitigation strategies based on rainfall prediction in Central Java.