A decade of rainfall, one risk map: GIS-based flood susceptibility mapping in a rapidly urbanizing coastal regency

Authors

  • Saepul Anwar Universitas Sultan Ageng Tirtayasa, Indonesia
    ID Indonesia
  • Yayat Ruhiat Universitas Sultan Ageng Tirtayasa, Indonesia
    ID Indonesia
  • Sjaifuddin Sjaifuddin Universitas Sultan Ageng Tirtayasa, Indonesia
    ID Indonesia

DOI:

https://doi.org/10.62672/joease.v4i3.300

Keywords:

CHRIPS, Flood, GIS, Rainfall, Tangerang Regency

Abstract

Flooding has become a major hydrometeorological hazard in rapidly developing urban areas, including Tangerang Regency, Banten Province, Indonesia. Increasing rainfall over recent years has intensified flood occurrences, highlighting the need for spatially informed flood-risk assessment. The study analyzed ten years (2015–2024) of rainfall observations obtained from the Budiarto Meteorological Station of the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG), complemented by Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite rainfall data for GIS-based spatial analysis. Statistical analysis was applied to evaluate rainfall patterns and temporal variability, while GIS-based spatial analysis was employed to delineate flood-prone zones. The results indicate an increasing trend in rainfall during the observation period, with peak rainfall occurring in February and March and average monthly rainfall ranging from 70.86 to 249.90 mm. Spatial analysis identified Kresek, Gunung Kaler, Mekar Baru, Kronjo, Kemiri, Mauk, Sukadiri, Pakuhaji, Teluknaga, and Kosambi as areas with high flood susceptibility. Overall, approximately 34.48% of the study area was classified as as a zone with a high level of flood susceptibility. The integration of long-term rainfall analysis with GIS-based flood mapping provides a robust spatial framework for understanding flood susceptibility in Tangerang Regency. These findings provide a GIS-based framework that can support flood-risk assessment, disaster mitigation planning, and sustainable land-use management in flood-prone regions.

References

Anastasya, F., & Wilantika, N. (2023). Pemetaan Kerawanan Banjir DAS Barito Tahun 2020. Berdasarkan Analisis Regresi Logistik Biner. Jurnal Wilayah Dan Lingkungan, 11(3), 245–263. https://doi.org/10.14710/jwl.11.3.245-263

Arifin, F., Muhibuddin, A., & Saleh, H. (2023). Mitigasi Kawasan Rawan Banjir Pada Daerah Aliran Sungai Jeneberang Dengan Menggunakan Sistem Informasi Geografis. Urban and Regional Studies Journal, 6(1), 71–82. https://doi.org/10.35965/ursj.v6i1.3813

Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., Van Dijk, A. I. J. M., Huffman, G. J., Adler, R. F., & Wood, E. F. (2019). Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS. Hydrology and Earth System Sciences, 23(1), 207–224. https://doi.org/10.5194/hess-23-207-2019

Costache, R., Arabameri, A., Costache, I., Crăciun, A., Md Towfiqul Islam, A. R., Abba, S. I., Sahana, M., & Pham, B. T. (2022). Flood susceptibility evaluation through deep learning optimizer ensembles and GIS techniques. Journal of Environmental Management, 316. https://doi.org/10.1016/j.jenvman.2022.115316

Fadhlina Mohd Anafi, N., Mohd Noor, N., Widyasamratri, H., & Mohn Noor, N. (2023). A SYSTEMATIC REVIEW OF REAL-TIME URBAN FLOOD FORECASTING MODEL IN MALAYSIA AND INDONESIA-CURRENT MODELLING AND CHALLENGE. In Pemodelan Jurnal Planologi (Vol. 20, Number 2).

Fura, A. N. A. J., Wiyono, R. U. A., & Indarto, I. (2020). Kecenderungan dan Perubahan Hujan Ekstrem Harian di Pulau Madura. Jurnal Ilmu Lingkungan, 18(1), 89–96. https://doi.org/10.14710/jil.18.1.89-96

Handika, R., Pratama, R. A., Ihsan, I. M., Adhi, R. P., Sabudin, Sundari, A., Sulistiawan, I. N., & Nugraha, Y. W. (2023). Identifying environmental variables in potential flood hazard areas using machine learning approach at Musi Banyuasin Regency, South Sumatra. IOP Conference Series: Earth and Environmental Science, 1201(1). https://doi.org/10.1088/1755-1315/1201/1/012037

Husein, M., Takele, T., Diriba, D., & Karuppannan, S. (2025). Flood hazard and risk assessment using GIS and remote sensing in the case of Ziway Lake watershed, central Main Ethiopian Rift. Environmental and Sustainability Indicators, 28. https://doi.org/10.1016/j.indic.2025.100920

Ikhwali, M. F., Nur, S., Darmansyah, D., Hamdan, A. M., Ersa, N. S., Aida, N., Yusra, A., & Satria, A. (2022). A review of climate change studies on paddy agriculture in Indonesia. IOP Conference Series: Earth and Environmental Science, 1116(1). https://doi.org/10.1088/1755-1315/1116/1/012052

Intergovernmental Panel on Climate Change. (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Core Writing Team, H. Lee, & J. Romero, Eds.). IPCC. https://doi.org/10.59327/IPCC/AR6-9789291691647

Mulsandi, A., Koesmaryono, Y., Hidayat, R., Faqih, A., & Sopaheluwakan, A. (2024). Detecting Indonesian Monsoon Signals and Related Features Using Space–Time Singular Value Decomposition (SVD). Atmosphere, 15(2). https://doi.org/10.3390/atmos15020187

Murali, K. V, & Srinivasan, K. (2025). GIS and AHP Based Flood Susceptibility Mapping: A Case Study of Chalakudy River Basin, Kerala, India. International Journal of Environment and Climate Change, 15(9), 18–34. https://doi.org/10.9734/ijecc/2025/v15i94992

Papalexiou, S. M., & Koutsoyiannis, D. (2013). Battle of extreme value distributions : A global survey on extreme daily rainfall. In Water Resources Research, 49(1). 187–201. https://doi.org/10.1029/2012WR012557

Ruhiat, Y. (2022). Forecasting rainfall and potential for repeated events to predict flood areas in Banten province, Indonesia. Journal of Measurements in Engineering, 10(2), 68–80. https://doi.org/10.21595/jme.2022.22363

Ruhiat, Y., Chaidir, J., Akbar, H., & Guntara, Y. (2025). Rainfall forecast and potential flooding of agricultural land in Serang Regency, Banten, Indonesia. Mathematical Models in Engineering, 11(2), 56–67. https://doi.org/10.21595/mme.2025.24796

Sandiwarno, S. (2024). Penerapan Machine Learning Untuk Prediksi Bencana Banjir. Jurnal Sistem Informasi Bisnis, 14(1), 62–76. https://doi.org/10.21456/vol14iss1pp62-76

Sunarti, E., Safitri, N. A., & Fitriyah, A. F. (2024). Sajira Flash Flood at January 2020: Analysis Its Prolonged Impact on Family Life Aspects. IOP Conference Series: Earth and Environmental Science, 1359(1). https://doi.org/10.1088/1755-1315/1359/1/012044

Supari, Tangang, F., Juneng, L., & Aldrian, E. (2017). Observed changes in extreme temperature and precipitation over Indonesia. International Journal of Climatology, 37(4), 1979–1997. https://doi.org/10.1002/joc.4829

Wartalska, K., Szymczewski, S., Domalewska, W., Wdowikowski, M., Przestrzelska, K., Kotowski, A., & Kaźmierczak, B. (2025). The Impact of Climate Change on the Functioning of Drainage Systems in Industrial Areas A Case Study. Atmosphere, 16(3). https://doi.org/10.3390/atmos16030347

Zevri, A. (2022). Studi Potensi Daerah Genangan Banjir Pasang (rob) Perairan Meulaboh dengan Sistem Informasi Geografis (SIG) Studi Potensi Daerah Genangan Banjir Pasang (rob) Perairan Meulaboh dengan Sistem Informasi Geografis (SIG) (Kajian Teknis). Jurnal Teknik Sipil, 28(3), 371–380. https://doi.org/10.5614/jts.2021.28.3.14

Downloads

Published

22-08-2026

How to Cite

Anwar, S., Ruhiat, Y., & Sjaifuddin, S. (2026). A decade of rainfall, one risk map: GIS-based flood susceptibility mapping in a rapidly urbanizing coastal regency. Journal of Environment and Sustainability Education, 4(3), 597–607. https://doi.org/10.62672/joease.v4i3.300

Issue

Section

Articles