SPATIAL ANALYSIS OF FLOOD SUSCEPTIBILITY AND ITS IMPACT ON EDUCATIONAL FACILITIES IN TANGERANG CITY
Abstract
Abstrak: Banjir merupakan bencana hidrometeorologi yang menyebabkan kerusakan luas serta menimbulkan kerugian di bidang ekonomi, kesehatan, dan pendidikan. Penelitian ini menganalisis distribusi spasial kerawanan banjir dan kaitannya dengan lokasi fasilitas pendidikan di Kota Tangerang. Pendekatan kuantitatif diterapkan dengan menggunakan metode Weighted Sum yang terintegrasi dalam Sistem Informasi Geografis (SIG) serta melibatkan berbagai parameter fisik dan antropogenik. Hasil analisis menunjukkan bahwa kelas kerawanan sedang dan tinggi mendominasi, masing-masing mencakup sekitar 6.300 ha dan 6.574 ha, sedangkan kelas sangat tinggi mencapai 2.022 ha. Sekitar 78,6% fasilitas pendidikan berada pada wilayah dengan tingkat risiko banjir sedang hingga sangat tinggi. Kecamatan dengan tingkat kerawanan tertinggi meliputi Tangerang, Ciledug, dan Pinang, sedangkan Batuceper, Neglasari, dan Benda tergolong rendah hingga sangat rendah. Temuan ini menegaskan bahwa curah hujan, topografi, jenis tanah, dan lahan terbangun berperan penting dalam menentukan kerawanan banjir, serta menjadi dasar mitigasi risiko fasilitas pendidikan dan kebijakan tata ruang wilayah.
Abstract: Floods are hydrometeorological disasters that cause extensive damage and lead to economic, health, and educational losses. This study analyzes the spatial distribution of flood susceptibility and its relationship with educational facilities in Tangerang City. A quantitative approach was applied using the Weighted Sum method integrated with Geographic Information Systems (GIS) and multiple physical and anthropogenic parameters. The results show that moderate and high susceptibility classes dominate, covering approximately 6,300 ha and 6,574 ha, while the very high class reaches 2,022 ha. About 78.6% of educational facilities are located in areas with moderate to very high flood risk. Districts with the highest susceptibility include Tangerang, Ciledug, and Pinang, whereas Batuceper, Neglasari, and Benda show low to very low risk. These findings highlight the interaction of rainfall, topography, soil type, and built-up land in shaping flood Susceptibility and provide a basis for educational facility risk mitigation and spatial planning policies.
Keywords
Full Text:
PDFReferences
Abebe, Y. A., Kabir, G., & Alam, T. (2021). Spatial flood risk assessment in urban areas using GIS and multi-criteria decision analysis: A case of Addis Ababa. Natural Hazards, 108(1), 475–497. https://doi.org/10.1007/s11069-021-04714-7
Abidin, Z., Hidayat, R., & Lestari, P. (2024). GIS-based flood vulnerability mapping using integrated topographic and hydrological parameters in Java, Indonesia. Hydrology Research, 55(2), 233–249. https://doi.org/10.2166/nh.2024.103
Adelekan, I. O., Ajibade, I., & Johnson, C. (2022). Urban flooding and climate change adaptation in African cities: Insights from Lagos and Nairobi. Climate Risk Management, 36. https://doi.org/10.1016/j.crm.2022.100420
Asfaw, T., Addis, H. K., & Melesse, A. M. (2023). Flood hazard mapping using multi-criteria and remote sensing data integration. Journal of Hydrology: Regional Studies.
Badan Meteorologi Klimatologi dan Geofisika. (2022). Data iklim Kota Tangerang tahun 2022. BMKG.
BNPB, P. (2025). Badan Nasional Penanggulangan Bencana. BNPB. https://www.bnpb.go.id/berita/dampak-banjir-di-jabodetabek-kerugian-ekonomi-dan-upaya-pemulihan
BPS. (2024). Kota Tangerang Dalam Angka. BPS-Statistics Tangerang Municipality. https://tangerangkota.bps.go.id/id/publication/2024/02/28/55863c9902e4a38181e35a35/kota-tangerang-dalam-angka-2024.html
Dewi, E., & Nurlela, E. (2023). Soil properties and their relation to flood susceptibility in urban areas. International Journal of Disaster Risk Reduction, 85, 103521. https://doi.org/10.1016/j.ijdrr.2023.103521
Dossa, K. F., Miassi, Y. E., & Bakary, S. (2025). Drowning in urban growth: Rethinking flood resilience and spatial equity in Lagos, Nigeria. Frontiers in Sustainable Resource Management, 1659930. https://doi.org/10.3389/fsrma.2025.1659930
Eastman, J. R. (2012). IDRISI Selva: Guide to GIS and Image Processing. Clark Labs, Clark University.
Fauzi, M. Z., & Handayani, T. (2024). The influence of land use and land cover changes on flood vulnerability assessment using GIS-based analysis. Journal of Environmental Management and Planning, 21(3), 305–320. https://doi.org/10.1016/j.jemp.2024.03.001
Gupta, L., & Dixit, J. (2023). Assessment of urban flood susceptibility and role of urban green space (UGS) on flooding susceptibility using GIS-based probabilistic models. Environmental Monitoring and Assessment, 195(12). https://doi.org/10.1007/s10661-023-12061-4
Guzha, A. C., Rufino, M. C., Okoth, S., Jacobs, S., & Nóbrega, R. L. B. (2022). Impacts of land use and hydrological variables on flood generation in urban landscapes. Water.
Haque, U., Hashizume, M., & Das, P. (2023). Spatial assessment of flood exposure in South and Southeast Asia: Implications for education and public health. Natural Hazards, 118(2), 213–229. https://doi.org/10.1007/s11069-023-05891-3
Hosseini, S. H., Kourgialas, N. N., & Singh, A. (2022). A comprehensive framework for urban flood vulnerability assessment using GIS and AHP methods. Water, 14(3), 431. https://doi.org/10.3390/w14030431
Ikhvan, A., & Mera, M. (2021). Case Study: Significant factors in hazard and vulnerability assessments in flood mitigation in Padang City. IOP Conf. Ser. Earth Environ. Sci., 708(1). https://doi.org/10.1088/1755-1315/708/1/012026
Islam, M. R., Hossain, M. A., & Das, S. (2023). Topographic and hydrological influence on flood hazard mapping: A case study from the Meghna Basin. Journal of Hydrology: Regional Studies, 48, 101166. https://doi.org/10.1016/j.ejrh.2023.101166
Kenyi, E. E. (2020). Public health impacts and responses to floods. South Sudan Medical Journal, 13(1), 22–23.
Khosravi, K., Pham, B. T., & Tien Bui, D. (2021). A comparison between statistical and machine learning models for flood susceptibility mapping: A case study of Iran. Catena, 198, 105048. https://doi.org/10.1016/j.catena.2020.105048
Kusumawati, R., Pratama, I. A., & Widyatmanti, W. (2024). Assessment of flood risk vulnerability of educational infrastructure using historical loss data and GIS. Natural Hazards, 120, 2500–2518. https://doi.org/10.1007/s11069-023-06123-x
Kwak, Y., Dwirahmadi, F., & Kim, J. (2023). Global trends and perspectives on flood vulnerability mapping: A systematic review. Progress in Disaster Science, 19. https://doi.org/10.1016/j.pdisas.2023.100282
Lancet Countdown. (2022). The 2022 report of the Lancet Countdown on health and climate change: Health at the mercy of fossil fuels. The Lancet. https://researchportal.tuni.fi/files/104334751/Lancet_Countdown_2022.pdf
Malczewski, J. (1999). GIS and Multicriteria Decision Analysis. John Wiley & Sons.
Malczewski, J. (2006). Ordered weighted averaging in GIS: A review and prospects. International Journal of Geographical Information Science, 20(5), 309–324. https://doi.org/10.1080/13658810600661508
Manna, H., Das, M., Pramanik, M., Sarkar, S., Mahato, S., Talukdar, S., Alkhuraiji, W. S., & Zhran, M. (2025). Ensemble intelligence for urban resilience: Flood susceptibility modeling in Mumbai using advanced machine learning. Geomatics, Natural Hazards and Risk, 16(1). https://doi.org/10.1080/19475705.2025.2588718
Marfai, M. A., Nugroho, Y., & Suryadi, D. (2021). Integrating flood risk analysis into spatial planning for education resilience in urban Indonesia. International Journal of Disaster Risk Reduction, 63, 102423. https://doi.org/10.1016/j.ijdrr.2021.102423
Pham, B. T., Pradhan, B., & Tien Bui, D. (2021). Flood hazard assessment using spatial multi-criteria decision analysis and machine learning models: A case study from Vietnam. Science of the Total Environment, 757, 143935. https://doi.org/10.1016/j.scitotenv.2020.143935
Pham, B. T., Shirzadi, A., & Chen, W. (2023). Advances in machine learning for flood susceptibility mapping and prediction: A global review. Progress in Physical Geography, 47(1), 3–28. https://doi.org/10.1177/03091333221109321
Prasetyo, A., & Wulandari, B. (2023). Slope analysis for flood hazard mapping in mountainous regions of Indonesia. Geosciences Research Journal, 12(4), 45–60. https://doi.org/10.1080/01490451.2023.2189012
Pribadi, C. B., Hariyanto, T., & Kurniawan, A. (2022). Analysis of Distribution of Flood Disaster Hazard in Tangerang City. IOP Conference Series: Earth and Environmental Science, 1039(1), 012049. https://doi.org/10.1088/1755-1315/1039/1/012049
Putra, D. W., Salim, W. A., Indradjati, P. N., & ... (2023). Understanding the position of urban spatial configuration on the feeling of insecurity from crime in public spaces. In Frontiers in Built …. frontiersin.org. https://doi.org/10.3389/fbuil.2023.1114968
Putra, R., & Prasetyo, L. B. (2021). Slope influence on surface runoff and flood potential in rapidly urbanizing regions. Remote Sensing Applications: Society and Environment, 23, 100528–100528. https://doi.org/10.1016/j.rsase.2021.100528
Rahman, F., Setiawan, B., & Utami, R. (2024). Spatial analysis of flood-prone areas in Tangerang City, Indonesia, using GIS and remote sensing. Natural Hazards Research, 4(2), 89–101. https://doi.org/10.1016/j.nhres.2024.02.005
Rustiadi, E., Panuju, D. R., & Pravitasari, A. E. (2015). Jabodetabek megacity: From city development toward urban complex management system. In Urban Development Challenges, Risks and Resilience in Asian Mega Cities (pp. 171–198). Springer. https://doi.org/10.1007/978-4-431-55043-3_9
Sahana, M., & Sajjad, H. (2022). GIS-based weighted overlay approach for flood susceptibility mapping in data-scarce regions. Environmental Challenges, 8, 100514. https://doi.org/10.1016/j.envc.2022.100514
Setiawan, Y., Hidayat, A., & Rahayu, S. (2023). River network density and its impact on urban flood routing: A case study in Southeast Asia. Water Resources Management, 37(1), 150–165. https://doi.org/10.1007/s11269-022-03355-y
Shafizadeh-Moghadam, H., Pourghasemi, H. R., & Tavakoli, M. (2023). Comparative assessment of GIS-based flood susceptibility models: Weighted overlay vs. Machine learning approaches. Environmental Modelling & Software, 160, 105533. https://doi.org/10.1016/j.envsoft.2023.105533
Sukri, N. S. M., Maulud, K. N. A., Rahman, S. A. F. S. A., Jaafar, W. S. W. M., Wan Mohtar, W. H. M., Khalid, R. M., Saudi, A. S. M., & Khan, M. N. (2025). Enhancing Urban Flood Vulnerability Mapping with Multi-Criteria Decision Analysis. Jurnal Kejuruteraan, 37(7), 3641–3654. https://doi.org/10.17576/jkukm-2025-37(7)-40
Susanti, D., & Nugroho, Y. (2023). Hydrological risk assessment and flood vulnerability mapping in Tangerang urban area. 1204, 012039. https://doi.org/10.1088/1755-1315/1204/1/012039
UNISDR. (2020). Words into Action: Nature-based solutions for disaster risk reduction. United Nations Office for Disaster Risk Reduction. https://www.undrr.org/publication/words-action-nature-based-solutions
Utami, R., Prasetyo, L., & Nugroho, Y. (2024). Flood susceptibility mapping using weighted overlay analysis and GIS in Tangerang City, Indonesia. 1220, 012045. https://doi.org/10.1088/1755-1315/1220/1/012045
Wibowo, G., Santoso, A., & Hadi, P. (2022). Rainfall variability and flood events: A GIS-based approach to vulnerability assessment. Hydrology Research Letters, 16(2), 125–135. https://doi.org/10.3315/hrl.2022.0012
WMO. (2021). Floods and droughts: Integrated flood management tools. World Meteorological Organization. https://library.wmo.int/doc_num.php?explnum_id=10859
Zheng, Y., Huang, Y., Liu, W., & Chen, J. (2020). Comparison of 1D and 2D urban flood modeling using integrated GIS and hydraulic model. Journal of Hydrology, 582, 124446. https://doi.org/10.1016/j.jhydrol.2020.124446
DOI: https://doi.org/10.31764/geography.v14i1.36733
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
GEOGRAPHY : Jurnal Kajian Penelitian & Pengembangan Pendidikan
Email: [email protected] | p-ISSN 2339-2835 | e-ISSN 2614-5529
EDITORIAL OFFICE: