Mapping of Potential Flood Prone Areas Using the Scoring Method and Overlay in Batanghari Regency
DOI:
https://doi.org/10.35895/jpsi.2.1.47-58.2026Keywords:
flood, maping, overlay method, scoring methodAbstract
Mapping of potential flood-prone areas using scoring and overlay methods in Batanghari Regency has been carried out. This study aims to determine the level of flood vulnerability and the distribution of flood-prone areas. The parameters used are rainfall parameters, soil type parameters, river distance parameters, slope parameters, land cover parameters, and elevation parameters. The methods used are scoring and overlay methods with the assistance of ArcGIS 10.8 software. The level of flood vulnerability is classified into three categories: not vulnerable, vulnerable, and highly vulnerable. The results obtained in this study show that the majority of Batanghari Regency has a flood vulnerability level in the not vulnerable class, covering an area of 397,158.03 Ha (72%), with areas in the vulnerable category covering 132,119.089 Ha (24.22%), and highly vulnerable areas covering 15,380.96 Ha (2.82%). In contrast, the area that is relatively safe from flooding is the Bajubang District, which covers an area of 102,592.1 hectares (90.17%). This indicates that some areas of Batanghari Regency are prone to flooding, making it very important to take disaster mitigation actions in the Batanghari Regency.
References
Aldrian, E., & Susanto, R. D. (2003). Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. International Journal of Climatology, 23(12), 1435–1452. https://doi.org/10.1002/joc.950
Aminoto, T., Susanti, N., Dani, R., & Faqih, A. (2026). Extreme Rainfall Indices Trends in Indonesia During 1971-2020. Jurnal Penelitian Pendidikan IPA, 12(3), 456-467.
Aminoto, T. (2024b). Performa Model Hujan CORDEX-SEA pada Aspek Spatial-Temporal dan Implementasi Faktor Bobot pada Rata-rata Ansambel. Repository of IPB University (Doctoral dissertation).
Aminoto, T., & Faqih, A. (2024c). Tren curah hujan di asia tenggara berdasarkan model Cordex-SEA dan data ERA5. Journal online of physics, 10(1), 7-13.
Aminoto, T., Faqih, A., Koesmaryono, Y., & Dasanto, B. D. (2024a). A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations. Agromet, 38(1), 19-35.
Anggraini, N., Pangaribuan, B., Siregar, A. P., Sintampalam, G., Muhammad, A., Damanik, M. R. S., & Rahmadi, M. T. (2021). Analisis Pemetaan Daerah Rawan Banjir Di Kota Medan Tahun 2020. Jurnal Samudra Geografi, 4(2), 27–33.
Aziza Nur Sitty, Somantri Lili, & Setiawan Iwan. (2021). Analisis pemetaan tingkat rawan banjir di Kecamatan Bontang Barat Kota Bontang berbasis sistem informasi geografis. Jurnal Pendidikan Geografi Undiksha, 9(2), 1–12.
Bookhagen, B., & Strecker, M. R. (2008). Orographic barriers, high-resolution TRMM rainfall, and relief variations along the eastern Andes. Geophysical Research Letters, 35(6), L06403. https://doi.org/10.1029/2007GL032011
Bradshaw, C. J. A., Sodhi, N. S., Peh, K. S.-H., & Brook, B. W. (2007). Global evidence that deforestation amplifies flood risk and severity. Global Change Biology, 13(11), 2379–2395. https://doi.org/10.1111/j.1365-2486.2007.01446.x
Darmawan, K., & Suprayogi, A. (2017). Analisis Tingkat Kerawanan Banjir di Kabupaten Sampang Menggunakan Metode Overlay Dengan Scoring Berbasis Sistem Informasi Geografis. Jurnal Geodesi Undip Januari (Vol. 6, Issue 1).
Ellis, E. C., & Ramankutty, N. (2008). Putting people in the map: Anthropogenic biomes of the world. Frontiers in Ecology and the Environment, 6(8), 439–447. https://doi.org/10.1890/070062
Gunawan, J., Hazriani, R., & Mahardika, R. Y. (2020). Morfologi dan Klasifikasi Tanah - Buku Ajar. Fakultas Pertanian Universitas Tanjungpura, April, 11.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., et al. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693
Hardiyatmo, H. C. 2019. Mekanika Tanah1. Yogyakarta. Gadjah Mada University Press.
Ka’u, A. A., Takumansang, E. D., & Sembel, A. (2021). Analisis Tingkat Kerawanan Banjir di Kecamatan Sangtombolang Kabupaten Bolaang Mongondow. Jurnal Spasial, 8(3), 291-302.
Kurnia, M. I., Mulki, G. Z., & Firdaus, H. (2019). Pemetaan Rawan Banjir di Kecamatan Pontianak Selatan dan Pontianak Tenggara Berbasis Sistem Informasi Geografis (SIG). Jurnal Untan, 6(2), 1–7.
Kusumo, P., & Nursari, E. (2016). Zonasi tingkat kerawanan banjir dengan sistem informasi geografis pada DAS Cidurian Kab. Serang, Banten. STRING (Satuan Tulisan Riset dan Inovasi Teknologi), 1(1).
Laila Nugraha, A. (2018). Peningkatan Akurasi dan Presisi Analisa Spasial Pemodelan Banjir Kota Semarang Menggunakan Kombinasi Sistem Informasi Geografis Dan Metode Logika Fuzzy. TEKNIK, 39(1), 16–24.
Merz, B., Thieken, A. H., & Gocht, M. (2007). Flood risk mapping at the local scale: Concepts and challenges. Natural Hazards and Earth System Sciences, 7(4), 497–510. https://doi.org/10.5194/nhess-7-497-2007
Nachappa, T. G., Piralilou, S. T., Ghorbanzadeh, O., Shahabi, H., & Blaschke, T. (2020). Flood susceptibility mapping using machine learning models: A review. Remote Sensing, 12(4), 1–23. https://doi.org/10.3390/rs12040635
Nandi Wardhana, P., Amini, S., Astuti, Y., & Kurnia, D. Pengaruh Perubahan Tutupan Lahan Terhadap Debit Banjir di DAS Winongo Derah Istimewa Yogyakarta. Jurnal Ilmiah Teknik Sipil, 22(2), 157-164.
Natannael, N., Panangian Sauduran Siahaan, J., Panji Winata, O., Ladya Sintari, C., Martha Wijaya, K., & Samuel Tubil, N. (2024). Analisis Pemetaan Daerah Rawan Banjir Di Kabupaten Katingan. JATI (Jurnal Mahasiswa Teknik Informatika), 8(4), 4550–4556.
Pryastuti, L. (2021). Pemetaan Tingkat Kerawananan Banjir di Kota Jambi Menggunakan Metode Scoring Dan Overlay Berbasis Sistem Informasi Geografis. Jurnal Ilmu Dan Inovasi Fisika, 5(2), 132–141.
Qian, J.-H., Robertson, A. W., & Moron, V. (2010). Interactions among ENSO, the monsoon, and diurnal cycle in rainfall variability over Java, Indonesia. Journal of the Atmospheric Sciences, 67(11), 3509–3524. https://doi.org/10.1175/2010JAS3348.1
Qin, Y., Liu, G., Zhang, Q., & Chen, Y. (2020). Soil properties and agricultural suitability of alluvial soils in river basins. Catena, 190, 104547. https://doi.org/10.1016/j.catena.2020.104547
Rinklebe, J., & Shaheen, S. M. (2017). Redox chemistry of wetland soils and its impact on contaminant dynamics. Environmental Science and Pollution Research, 24, 10524–10529. https://doi.org/10.1007/s11356-017-8473-x
Sholikhan, M., Prasetyo, S. Y. J., & Hartomo, K. D. (2019). Pemanfaatan WebGIS untuk Pemetaan Wilayah Rawan Longsor Kabupaten Boyolali dengan Metode Skoring dan Pembobotan. Jurnal Teknik Informatika Dan Sistem Informasi, 5(1), 131–143.
Simanjuntak, Y. S. M., Suwarman, R., & Edi, R. (2023). Analisis Karasteristik Curah Hujan Penyebab Banjir Berdurasi Panjang (Studi Kasus: Banjir Tahun 2019 di Baleendah, Jawa Barat). Jurnal Sumber Daya Air. 19(1), 29–41.
Tanesib, J. L., Warsito, A., & Nuryanti. (2018). Pemetaan Daerah Rawan Banjir Dengan Penginderaan Kupang Timur Kabupaten Kupang Provinsi Nusa Tenggara Timur. Jurnal Fisika, 3(1), 73–79.
Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008
Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2015). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008
Ward, P. J., Jongman, B., Weiland, F. S., Bouwman, A., Van Beek, R., Bierkens, M. F. P., Ligtvoet, W., & Winsemius, H. C. (2013). Assessing flood risk at the global scale: Model setup, results, and sensitivity. Environmental Research Letters, 8(4), 044019. https://doi.org/10.1088/1748-9326/8/4/044019
Yao, L., Wei, W., & Chen, L. (2016). How does imperviousness impact the urban rainfall-runoff process under various storm cases? Ecological Indicators, 60, 893–905. https://doi.org/10.1016/j.ecolind.2015.08.041
Zhang, H., Schroder, J. L., Pittman, J. J., Wang, J., & Payton, M. E. (2021). Soil nutrient availability and management in highly weathered soils. Geoderma, 401, 115327. https://doi.org/10.1016/j.geoderma.2021.115327
Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94. https://doi.org/10.1016/j.rse.2011.10.028
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Melka Sintia Siburian, Tugiyo Aminoto, Husnul Hamdi (Author)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


