BibTex Citation Data :
@article{geoplanning80998, author = {Hasan Nugraha and M. Angga Pratama and Muhammad Suhartono and Anjar Sakti and Ketut Wikantika}, title = {Integrated Flood Risk Mapping and Hazard-Vulnerability Assessment for Mitigation Prioritization in Sumatra}, journal = {Geoplanning: Journal of Geomatics and Planning}, volume = {13}, number = {1}, year = {2026}, keywords = {Flood Risk Assessment; Bivariate Hazard-Vulnerability Matrix; Mitigation Prioritization; Geospatial Modeling}, abstract = { Flood risk in tropical regions such as Sumatra is increasing due to intensified rainfall extremes and rapid urbanization. Although flood hazard mapping is widely applied, many studies do not clearly distinguish physical hazard from socio-economic vulnerability, limiting their usefulness for targeted mitigation. This study proposes an integrated geospatial framework combining multi-parameter flood hazard assessment and a socio-demographic vulnerability index through a bivariate hazard–vulnerability matrix to support risk reduction. The framework was applied to the November 2025 flood events in Aceh, North Sumatra, and West Sumatra, using nine hazard parameters and four vulnerability indicators. Results show that High to Very High Hazard zones cover 21% of the study area, in lowland basins, while 12% of the area falls into the Very High-Risk category. Validation using observed damage data shows that medium-risk (Risk 2) zones, rather than only the highest-risk cores, account for 87.5%–100% of the exposed population across five major cities, capturing 97.5% of affected residents in Aceh Tamiang. The proposed 5×5 bivariate matrix separates risk dominated by physical hazard, socio-economic vulnerability, or their interaction This enables stratified mitigation strategies, ranging from integrated structural and social interventions to targeted engineering and community-based measures, while providing spatially explicit guidance to strengthen flood risk management and support Sendai Framework objectives under climate change. }, issn = {2355-6544}, doi = {10.14710/geoplanning.13.1.%p}, url = {https://ejournal.undip.ac.id/index.php/geoplanning/article/view/80998} }
Refworks Citation Data :
Flood risk in tropical regions such as Sumatra is increasing due to intensified rainfall extremes and rapid urbanization. Although flood hazard mapping is widely applied, many studies do not clearly distinguish physical hazard from socio-economic vulnerability, limiting their usefulness for targeted mitigation. This study proposes an integrated geospatial framework combining multi-parameter flood hazard assessment and a socio-demographic vulnerability index through a bivariate hazard–vulnerability matrix to support risk reduction. The framework was applied to the November 2025 flood events in Aceh, North Sumatra, and West Sumatra, using nine hazard parameters and four vulnerability indicators. Results show that High to Very High Hazard zones cover 21% of the study area, in lowland basins, while 12% of the area falls into the Very High-Risk category. Validation using observed damage data shows that medium-risk (Risk 2) zones, rather than only the highest-risk cores, account for 87.5%–100% of the exposed population across five major cities, capturing 97.5% of affected residents in Aceh Tamiang. The proposed 5×5 bivariate matrix separates risk dominated by physical hazard, socio-economic vulnerability, or their interaction This enables stratified mitigation strategies, ranging from integrated structural and social interventions to targeted engineering and community-based measures, while providing spatially explicit guidance to strengthen flood risk management and support Sendai Framework objectives under climate change.
Article Metrics:
Aeni, P., & Anwar, K. M. (2024). Hydrometeorological Disaster: Challenges and Mitigation in Indonesia. Jurnal Indonesia Sosial Teknologi, 5(01), 318–330. [https://doi.org/10.59141/jist.v5i01.888">Crossref]
Ahmed, I., Das (Pan), N., Debnath, J., Bhowmik, M., & Bhattacharjee, S. (2024). Flood hazard zonation using GIS-based multi-parametric Analytical Hierarchy Process. Geosystems and Geoenvironment, 3(2). [https://doi.org/10.1016/j.geogeo.2023.100250">Crossref]
Ali, S. A., Khatun, R., Ahmad, A., & Ahmad, S. N. (2019). Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Modeling Earth Systems and Environment, 5(3), 1083–1102. [https://doi.org/10.1007/s40808-019-00593-z">Crossref]
Ariska, M., Suhadi, Supari, Irfan, M., & Iskandar, I. (2024). Spatio-Temporal Variations of Indonesian Rainfall and Their Links to Indo-Pacific Modes. Atmosphere, 15(9). [https://doi.org/10.3390/atmos15091036">Crossref]
Ariyani, D., Purwanto, M. Y. J., Sunarti, E., Perdinan, & Juniati, A. T. (2024). Integrated flood hazard assessment using multi-criteria analysis and geospatial modeling. Journal of Degraded and Mining Lands Management, 11(4), 6121–6134. [https://doi.org/10.15243/jdmlm.2024.114.6121">Crossref]
Arrighi, C., Pregnolato, M., & Castelli, F. (2021). Indirect flood impacts and cascade risk across interdependent linear infrastructures. Natural Hazards and Earth System Sciences, 21(6), 1955–1969. [https://doi.org/10.5194/nhess-21-1955-2021">Crossref]
Asaaga, F. A., Purse, B. V., Rahman, M., Srinivas, P. N., Kalegowda, S. D., Seshadri, T., Young, J. C., & Oommen, M. A. (2023). The role of social vulnerability in improving interventions for neglected zoonotic diseases: The example of Kyasanur Forest Disease in India. PLOS Global Public Health, 3(2), 1–28. [https://doi.org/10.1371/journal.pgph.0000758">Crossref]
Badan Meteorologi Klimatologi dan Geofisika (BMKG). (2025). Citra Himawari-9 IR Enhanced – Indonesia. BMKG. https://www.bmkg.go.id/cuaca/satelit/himawari-ir-enhanced">
Badan Nasional Penanggulangan Bencana (BNPB). (2025). Rekapitulasi Terdampak Bencana Sumatera 2025. BNPB Geoportal. https://gis.bnpb.go.id/BANSORSUMATERA2025/">
Bui, D. T., Ngo, P. T. T., Pham, T. D., Jaafari, A., Minh, N. Q., Hoa, P. V., & Samui, P. (2019). A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena, 179(October 2018), 184–196. [https://doi.org/10.1016/j.catena.2019.04.009">Crossref]
de Ruiter, M. C., Couasnon, A., van den Homberg, M. J. C., Daniell, J. E., Gill, J. C., & Ward, P. J. (2020). Why We Can No Longer Ignore Consecutive Disasters. Earth’s Future, 8(3). [https://doi.org/10.1029/2019EF001425">Crossref]
Diriba, D., Takele, T., Karuppannan, S., & Husein, M. (2024). Flood hazard analysis and risk assessment using remote sensing, GIS, and AHP techniques: a case study of the Gidabo Watershed, main Ethiopian Rift, Ethiopia. Geomatics, Natural Hazards and Risk, 15(1). [https://doi.org/10.1080/19475705.2024.2361813">Crossref]
Dulawan, J. M. T., Imamura, Y., Konishi, T., Amaguchi, H., & Ohara, M. (2024). A systematic framework for assessing social vulnerability to flood for integrated flood risk management: A case study in Metro Manila, Philippines. International Journal of Disaster Risk Reduction, 112(August), 104778. [https://doi.org/10.1016/j.ijdrr.2024.104778">Crossref]
Ekmekcioğlu, Ö., Koc, K., & Özger, M. (2021). District based flood risk assessment in Istanbul using fuzzy analytical hierarchy process. Stochastic Environmental Research and Risk Assessment, 35(3), 617–637. [https://doi.org/10.1007/s00477-020-01924-8">Crossref]
European Commission. (2025). Indonesia, Thailand, Malaysia | Severe weather and floods - DG ECHO Daily Map | 18/12/2025. Reliefweb. https://reliefweb.int/map/indonesia/indonesia-thailand-malaysia-severe-weather-and-floods-dg-echo-daily-map-18122025">
Ferreira, T. M. (2020). Recent advances in the assessment of flood risk in urban areas. Water (Switzerland), 12(7), 10–12. [https://doi.org/10.3390/w12071865">Crossref]
Gomez Vaca, A. N., Popartan, L. A., Nuss-Girona, S., & Rodríguez-Roda, I. (2025). Spatial approach for assessing vulnerability to urban flooding: a proposal for a multidimensional index. Natural Hazards, 121(14), 16799–16825. [https://doi.org/10.1007/s11069-025-07451-5">Crossref]
He, L., Li, Q., Zhang, J., Deng, X., Wu, Z., Wang, Y., Chan, P. W., & Li, N. (2024). Enhanced Tropical Cyclone Precipitation Prediction in the Northwest Pacific Using Deep Learning Models and Ensemble Techniques. Water (Switzerland), 16(5). [https://doi.org/10.3390/w16050671">Crossref]
Ibrahim, M., Huo, A., Ullah, W., Ullah, S., & Xuantao, Z. (2024). An integrated approach to flood risk assessment using multi-criteria decision analysis and geographic information system. A case study from a flood-prone region of Pakistan. Frontiers in Environmental Science, 12(January), 1–16. [https://doi.org/10.3389/fenvs.2024.1476761">Crossref]
Intergovernmental Panel on Climate Change (IPCC). (2023). Climate Change 2021 – The Physical Science Basis. In Climate Change 2021 – The Physical Science Basis. [https://doi.org/10.1017/9781009157896">Crossref]
Islam, T., Zeleke, E. B., Afroz, M., & Melesse, A. M. (2025). A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches. Remote Sensing, 17(3). [https://doi.org/10.3390/rs17030524">Crossref]
Istiawan, D., Wulandari, R., & Sulastri. (2023). Mapping of Social Vulnerability to Natural Hazards in Geodemographic Analysis Using Fuzzy Geographically Weighted Clustering. Scientific Journal of Informatics, 10(4), 413–422. [https://journal.unnes.ac.id/nju/sji/article/view/47418">Crossref]
Jumadi, J., Danardono, D., Priyono, K. D., Roziaty, E., Masruroh, H., Rohman, A., Amin, C., Hadibasyir, H. Z., Fikriyah, V. N., Nawaz, M., Sattar, F., & Lotfata, A. (2024). Utilizing Open Access Spatial Data for Flood Risk Mapping: A Case Study in the Upper Solo Watershed. Geoplanning, 11(2), 189–204. [https://doi.org/10.14710/geoplanning.11.2.189-204">Crossref]
Lanlan, J., Sarker, M. N. I., Ali, I., Firdaus, R. B. R., & Hossin, M. A. (2024). Vulnerability and resilience in the context of natural hazards: a critical conceptual analysis. Environment, Development and Sustainability, 26(8), 19069–19092. [https://doi.org/10.1007/s10668-023-03440-5">Crossref]
Leal, M., Reis, E., Pereira, S., & Santos, P. P. (2021). Physical vulnerability assessment to flash floods using an indicator-based methodology based on building properties and flow parameters. Journal of Flood Risk Management, 14(3), 1–19. [https://doi.org/10.1111/jfr3.12712">Crossref]
Magnini, A., Lombardi, M., Bujari, A., Mattivi, P., Shustikova, I., Persiano, S., Patella, M., Bitelli, G., Bellavista, P., Lo Conti, F., Tirri, A., Bagli, S., Mazzoli, P., & Castellarin, A. (2023). Geomorphic flood hazard mapping: from floodplain delineation to flood hazard characterization. Hydrological Sciences Journal, 68(16), 2388–2403. [https://doi.org/10.1080/02626667.2023.2269909">Crossref]
Megahed, H. A., Abdo, A. M., AbdelRahman, M. A. E., Scopa, A., & Hegazy, M. N. (2023). Frequency Ratio Model as Tools for Flood Susceptibility Mapping in Urbanized Areas: A Case Study from Egypt. Applied Sciences (Switzerland), 13(16). [https://doi.org/10.3390/app13169445">Crossref]
Mondal, A., & Garg, R. D. (2025). Global urban flood dynamics and future vulnerability assessment: a comprehensive study of trends, predictions, and mitigation. Natural Hazards, 121(9), 11187–11207. [https://doi.org/10.1007/s11069-025-07288-y">Crossref]
Nguyen, D. L., Chou, T. Y., Hoang, T. V., & Chen, M. H. (2023). Flood Susceptibility Mapping Using Machine Learning Algorithms: A Case Study in Huong Khe District, Ha Tinh Province, Vietnam. International Journal of Geoinformatics, 19(7), 1–15. [https://doi.org/10.52939/ijg.v19i7.2739">Crossref]
Pakati, S. S., Shoko, C., & Dube, T. (2025). Integrated flood modelling and risk assessment in urban areas: A review on applications, strengths, limitations and future research directions. Journal of Hydrology: Regional Studies, 61(July), 102583. [https://doi.org/10.1016/j.ejrh.2025.102583">Crossref]
Parizi, S. M., Taleai, M., & Sharifi, A. (2022). A GIS-Based Multi-Criteria Analysis Framework to Evaluate Urban Physical Resilience against Earthquakes. Sustainability (Switzerland), 14(9). [https://doi.org/10.3390/su14095034">Crossref]
Parsian, S., Amani, M., Moghimi, A., Ghorbanian, A., & Mahdavi, S. (2021). Flood hazard mapping using fuzzy logic, analytical hierarchy process, and multi-source geospatial datasets. Remote Sensing, 13(23). [https://doi.org/10.3390/rs13234761">Crossref]
Purwanto, A., Rustam, Eviliyanto, & Andrasmoro, D. (2022). Flood Risk Mapping Using GIS and Multi-Criteria Analysis at Nanga Pinoh West Kalimantan Area. Indonesian Journal of Geography, 54(3), 463–470. [https://doi.org/10.22146/IJG.69879">Crossref]
Rahman, M. M., Tanni, K. N., Shobuj, I. A., Hossain, M. T., Alam, E., Al Hattaw, K. S., & Islam, M. K. (2025). Multidimensional Vulnerability Assessment for Floods: Evidence From Flood-Prone Areas of Bangladesh. Journal of Flood Risk Management, 18(3). [https://doi.org/10.1111/jfr3.70089">Crossref]
Ramadhan, R., Marzuki, M., Suryanto, W., Sholihun, S., Yusnaini, H., Muharsyah, R., & Hanif, M. (2022). Trends in rainfall and hydrometeorological disasters in new capital city of Indonesia from long-term satellite-based precipitation products. Remote Sensing Applications: Society and Environment, 28(July), 100827. [https://doi.org/10.1016/j.rsase.2022.100827">Crossref]
Rufat, S., Tate, E., Emrich, C. T., & Antolini, F. (2019). How Valid Are Social Vulnerability Models? Annals of the American Association of Geographers, 109(4), 1131–1153. [https://doi.org/10.1080/24694452.2018.1535887">Crossref]
Saha, A., Pal, S. C., Arabameri, A., Blaschke, T., Panahi, S., Chowdhuri, I., Chakrabortty, R., Costache, R., & Arora, A. (2021). Flood susceptibility assessment using novel ensemble of hyperpipes and support vector regression algorithms. Water (Switzerland), 13(2), 1–27. [https://doi.org/10.3390/w13020241">Crossref]
Sakti, A. D., Deliar, A., Hafidzah, D. R., Chintia, A. V., Anggraini, T. S., Ihsan, K. T. N., Virtriana, R., Suwardhi, D., Harto, A. B., Nurmaulia, S. L., Aritenang, A. F., Riqqi, A., Hernandi, A., Soeksmantono, B., & Wikantika, K. (2024). Machine learning based urban sprawl assessment using integrated multi-hazard and environmental-economic impact. Scientific Reports, 14(1), 1–16. [https://doi.org/10.1038/s41598-024-62001-6">Crossref]
Santi, H., Ginting, B., Sitorus, H., Ismail, R., & Manurung, R. (2022). Social Capital in Mitigating Flood Disaster in the People of Aceh Tenggara, Indonesia. Open Access Indonesia Journal of Social Sciences, 5(3), 758–764. [https://doi.org/10.37275/oaijss.v5i3.117">Crossref]
Sigit, A., Koyama, M., & Harada, M. (2023). Flood Risk Assessment Focusing on Exposed Social Characteristics in Central Java, Indonesia. Sustainability (Switzerland), 15(24). [https://doi.org/10.3390/su152416856">Crossref]
Taoukidou, N., Karpouzos, D., & Georgiou, P. (2025). Flood Hazard Assessment Through AHP, Fuzzy AHP, and Frequency Ratio Methods: A Comparative Analysis. Water (Switzerland), 17(14). [https://doi.org/10.3390/w17142155">Crossref]
Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., & Slayback, D. A. (2021). Satellite imaging reveals increased proportion of population exposed to floods. Nature, 596(7870), 80–86. [https://doi.org/10.1038/s41586-021-03695-w">Crossref]
Verma, P., Khan, A. S., & Safiullah, Z. N. A. (2025). Urban Flood Resilience in India: A Comprehensive Review of Challenges, Assessment Strategies, and Future Directions. Journal of Environment and Bio-Science, 39(01), 69. [https://doi.org/10.59467/jebs.2025.39.69">Crossref]
Last update:
Last update: 2026-06-17 12:17:50
Start from Volume 11 Number 2 2024, authors who publish in the Geoplanning Journal of Geomatics and Planning retain full copyright ownership (Copyright@Author) of their work. In keeping with the journal’s commitment to open access, all articles are published under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).Before Volume 11 Number 2 2024, copyright was still owned by the publisher, Universitas Diponegoro. To support a better open access program, we have created a new policy so that authors have full copyright ownership.
Readers and third parties may also use the content in accordance with the CC BY-NC-SA license. This includes the ability to reproduce, modify, and build upon the article, as long as proper attribution is given, and any resulting work is distributed under the same license.
