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This is a Quantitative Study study.

October 2, 2025

Flood Risk Clustering Based on SARIMA Rainfall Prediction and Regional Mapping in Central Java

WMWildatul Maulidiyah HRHazelita Dwi Rahmasari KNKhairil Anwar NotodiputroIPB University

Key Points

Clustering identified three rainfall intensity groups, aiding in flood risk classification.
The SARIMA model achieved a MAPE of 9.4% for medium rainfall intensity in Cluster 2.
Dynamic Time Warping facilitated precise clustering based on rainfall patterns across the region.
Findings provide valuable insights for flood risk mitigation and planning in Central Java.

Abstract

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.

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