Hybrid Fuzzy-AHP and Machine Learning with Sensitivity Analysis for Urban Flood Risk Assessment
Article Sidebar
Main Article Content
Abstract
Urban flooding poses a growing challenge in rapidly urbanizing regions due to the combined effects of climate variability, land-use change, and infrastructure limitations. This study proposes a hybrid framework integrating the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP), ensemble machine learning, and sensitivity analysis to support urban flood risk assessment. Fuzzy-AHP is employed to incorporate expert judgment and address uncertainty through triangular fuzzy numbers, while Random Forest and XGBoost are used to capture non-linear relationships and temporal patterns in heterogeneous flood-related data. The framework is applied to 1,008 observations from 12 districts in Bekasi City, Indonesia, covering the period 2018–2024. Model performance indicates strong discriminatory capability in distinguishing flood and non-flood conditions. Sensitivity analysis is explicitly positioned as a policy-oriented diagnostic and prioritization tool, enabling the identification of influential variables relevant for seasonal planning and early warning strategies. The results highlight the dominant role of climate-related factors, particularly rainfall and temporal variables, in shaping urban flood risk. Overall, the proposed framework demonstrates the complementary integration of expert knowledge and data-driven learning, offering a transferable methodological reference for flood risk assessment in complex urban environments.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
How to Cite
References
Benaiche, M., Mokhtari, E., Berghout, A., Abdelkebir, B., & Engel, B. (2025). Sensitivity of flood-prone areas to extreme rainfall using ahp and fuzzy ahp: a case study of boussellam and k’sob watersheds, algeria. Journal of Water and Climate Change, 16(6), 1948–1968. https://doi.org/10.2166/wcc.2025.520
Cikmaz, B. A., Yildirim, E., & Demir, I. (2025). Flood susceptibility mapping using fuzzy analytical hierarchy process for cedar rapids, Iowa. International Journal of River Basin Management, 23(1), 1–13. https://doi.org/10.1080/15715124.2023.2216936
Coffey, L., & Claudio, D. (2021). In defense of group fuzzy ahp: a comparison of group fuzzy ahp and group ahp with confidence intervals. Expert Systems with Applications, 178, 114970. https://doi.org/10.1016/j.eswa.2021.114970
Demirel, B., Yildirim, E., & Can, E. (2025). Gis-based landslide susceptibility mapping using ahp, fmea, and pareto systematic analysis in central yalova, türkiye. Engineering Science and Technology, an International Journal, 64, 102013. https://doi.org/10.1016/j.jestch.2025.102013
Ekmekcioğlu, Ö., Koc, K., & Özger, M. (2021). Stakeholder perceptions in flood risk assessment: a hybrid fuzzy ahp-topsis approach for istanbul, turkey. International Journal of Disaster Risk Reduction, 60, 102327. https://doi.org/10.1016/j.ijdrr.2021.102327
Ghasemzadeh, B., Zarabadi, Z. S. S., Majedi, H., Behzadfar, M., & Sharifi, A. (2021). A framework for urban flood resilience assessment with emphasis on social, economic and institutional dimensions: a qualitative study. Sustainability, 13(14), 7852. https://doi.org/10.3390/su13147852
Guan, X., Yu, F., Xu, H., Li, C., & Guan, Y. (2024). Flood risk assessment of urban metro system using random forest algorithm and triangular fuzzy number based analytical hierarchy process approach. Sustainable Cities and Society, 109, 105546. https://doi.org/10.1016/j.scs.2024.105546
He, W., Zeng, J., Zhang, J., Namaiti, A., Shi, W., Song, Y., & Tian, J. (2025). Dynamic urban flood risk assessment based on human activity patterns: an ifahp-ewm-topsis approach. Sustainable Cities and Society, 133, 106832. https://doi.org/10.1016/j.scs.2025.106832
Hidayatulloh, A., & Bahrawi, J. (2025). Flood risk assessment under climate change scenarios in the wadi ibrahim watershed. Hydrology, 12(5), 120. https://doi.org/10.3390/hydrology12050120
Huang, X., & Wang, D. (2025). Urban flood resilience in a megacity context: multidimensional assessment and spatial differentiation in shenzhen. Sustainability, 17(17), 7852. https://doi.org/10.3390/su17177852
Khumaidi, A., Raafi’udin, R., & Triastuti, N. S. (2024). Enhancing ship coating quality detection via machine learning-optimized visible near-infrared spectroscopy. Instrumentation Mesure Metrologie, 23(6), 441–450. https://doi.org/10.18280/i2m.230604
Liu, Q. (2022). Identifying and correcting the defects of the saaty analytic hierarchy/network process: a comparative study of the saaty analytic hierarchy/network process and the markov chain-based analytic network process. Operations Research Perspectives, 9, 100244. https://doi.org/10.1016/j.orp.2022.100244
Mujib, M. A., Apriyanto, B., Kurnianto, F. A., Ikhsan, F. A., Nurdin, E. A., Pangastuti, E. I., & Astutik, S. (2021). Assessment of flood hazard mapping based on analytical hierarchy process (ahp) and gis: application in kencong district, jember regency, indonesia. Geosfera Indonesia, 6(3), 353. https://doi.org/10.19184/geosi.v6i3.21668
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), 187. https://doi.org/10.3390/atmos15020187
Mushwani, H., Ahmadzai, M. R., Ullah, H., Baheer, M. S., & Peroz, S. (2024). A comprehensive ahp numerical module for assessing resilience of kabul city to flood hazards. Urban Climate, 55, 101939. https://doi.org/10.1016/j.uclim.2024.101939
Perdinan, Ryco, F. A., Syafararisa, D. P., Suvany, A., Sabilla, C. J., Revia, M., & Ikrom, M. (2023). Tidal flood hazard assessment in pekalongan city, central java. IOP Conference Series: Earth and Environmental Science, 1266(1), 012058. https://doi.org/10.1088/1755-1315/1266/1/012058
Pimenta, L., Duarte, L., Teodoro, A. C., Beltrão, N., Gomes, D., & Oliveira, R. (2025). Gis-based flood susceptibility mapping using ahp in the urban amazon: a case study of ananindeua, brazil. Land, 14(8), 1543. https://doi.org/10.3390/land14081543
Prashar, N., Lakra, H. S., Shaw, R., & Kaur, H. (2023). Urban flood resilience: a comprehensive review of assessment methods, tools, and techniques to manage disaster. Progress in Disaster Science, 20, 100299. https://doi.org/10.1016/j.pdisas.2023.100299
Pugara, A., Pradana, B., & Puspasari, D. A. (2021). The impact of the land-use changes on the water carrying capacity in kajen, indonesia: a spatial analysis. IOP Conference Series: Earth and Environmental Science, 887(1), 012018. https://doi.org/10.1088/1755-1315/887/1/012018
Rana, I. A., & Routray, J. K. (2018). Multidimensional model for vulnerability assessment of urban flooding: an empirical study in pakistan. International Journal of Disaster Risk Science 2018 9:3, 9(3), 359–375. https://doi.org/10.1007/s13753-018-0179-4
Sar, N., Ryngnga, P. K., & De, D. K. (2025). Application of the analytical hierarchy process (ahp) for flood susceptibility mapping using gis techniques in lower reach of keleghai river basin, west bengal, india. Geohazard Mechanics, 3(2), 123–135. https://doi.org/10.1016/j.ghm.2025.06.002
Taşkın, H. F., & Manioğlu, G. (2024). Evaluation of the impact of land use ratios and cover materials in settlement design on stormwater runoff. Land Use Policy, 146, 107314. https://doi.org/10.1016/j.landusepol.2024.107314
Tierolf, L., de Moel, H., & van Vliet, J. (2021). Modeling urban development and its exposure to river flood risk in southeast asia. Computers, Environment and Urban Systems, 87, 101620. https://doi.org/10.1016/j.compenvurbsys.2021.101620
Toth, W., & Vacik, H. (2018). A comprehensive uncertainty analysis of the analytic hierarchy process methodology applied in the context of environmental decision making. Journal of Multi-Criteria Decision Analysis, 25(5–6), 142–161. https://doi.org/10.1002/mcda.1648
Wieckowski, J., Kizielewicz, B., Watrobski, J., & Salabun, W. (2024). A new approach for handling uncertainty of expert judgments in complex decision problems. IEEE Access, 12, 142026–142046. https://doi.org/10.1109/access.2024.3445265
Xu, Y., Yang, Y., Wang, Z., Xiong, J., Yong, Z., Zhang, X., Liu, J., Chen, G., Zhao, Q., Hao, J., Xu, G., & Zhu, A. (2024). Dynamic response of flood risk in urban-township complex to future uncertainty. International Journal of Disaster Risk Reduction, 114, 104999. https://doi.org/10.1016/j.ijdrr.2024.104999
Yuanita, C. N., & Sagala, S. (2025). Blue-green infrastructure in jakarta’s fringe: an analysis of accessibility to blue-green spaces as a flood solution in bekasi city. International Journal of Disaster Risk Reduction, 121, 105425. https://doi.org/10.1016/j.ijdrr.2025.105425
Zhou, S., Geng, X., Jia, W., Xu, H., Xu, X., Chen, H., Wang, M., & Wu, Z. (2025). Towards climate-adaptive equality in coastal megacities: assessing urban flooding risk disparities and nonlinear effect of multidimensional indicators through an interpretable lightgbm-shap framework. Sustainable Cities and Society, 132, 106809. https://doi.org/10.1016/j.scs.2025.106809