Flood Risk Analysis Using Spatial Synthetic Population in the Upper Bengawan Solo Watershed, Indonesia
DOI:
https://doi.org/10.23917/forgeo.13611Keywords:
Spatial synthetic population, Global Human Settlement Layer, Flood risk, Upper Bengawan Solo, AHP, Social vulnerability, flood mitigationAbstract
This study develops a spatial synthetic population (SSP)-based computational model to produce realis-tic, high-resolution flood-risk maps for the Upper Bengawan Solo Watershed. It combines Global Hu-man Settlement (spatial distribution) with local population statistics (attributes). The SSP is created for flood risk mapping in the Upper Bengawan Solo Watershed (BSH) using a 100 m grid from the Global Human Settlement Layer (GHSL) GHS-POP R2023A. Synthetic individuals are strategically placed around the pixel centre (radius ≤ 100 m), and each is assigned demographic attributes (age, gender, ed-ucation, occupation) validated against official county-level data. Social vulnerability is calculated through weighted aggregation (AHP) across four attributes; individual scores are combined with flood hazard intensity at each location to produce a risk index for each person. Validation shows that (i) the SSP aligns closely with reference statistics: gender and age are nearly identical (MAE ≈ 0.01–0.02%), with slight deviations in occupation (MAE 6.52%) and education (MAE 4.89%), (ii) the overall suita-bility of the SSP compared to GHS counts at pixel samples, and (iii) location plausibility testing using ESRI Sentinel‑2 Land Cover (10 m). Results indicate that (i) the SSP aligns well for gender, moderately for education and occupation, but shows significant misalignment in age, (ii) 91.96% of SSP points are in built-up land, suggesting high spatial accuracy. Medium- to high-risk patterns are mainly along the main river corridors and peri-urban areas, while rural non-built zones are mostly low- to medium-risk. These findings suggest that this methodology is scalable, reproducible, and suitable for data limited regions, enabling the production of detail risk maps that can guide mitigation and preparedness efforts.
Downloads
References
Abdelkareem, M., & Mansour, A. M. (2023). Risk assessment and management of vulnerable areas to flash flood ha-zards in arid regions using remote sensing and GIS-based knowledge-driven techniques. Natural Hazards, 117(3), 2269–2295. doi: 10.1007/s11069-023-05942-x [Google Scholar] [CrossRef]
Absori, A., Hernanda, T., Fitriciada, A., Wardiono, K., & Budiono, A. (2023). Analysis of the Issues on Bengawan Solo River Basin Management Policies. WSEAS Transactions on Environment and Development, 19, 25–32. [Google Scholar]
Agriesti, S., Roncoli, C., & Nahmias-Biran, B. (2022). Assignment of a synthetic population for activity-based modeling employing publicly available data. ISPRS International Journal of Geo-Information, 11(2), 148. [Google Scholar]
Anna, A. N., & Priyana, Y. (2015). Kajian biofisik lahan untuk penilaian kerentanan banjir di DAS Bengawan Solo Hu-lu. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan, 8(1), 96-108. doi: 10.29244/jpsl.8.1.96-108 [Google Scholar] [CrossRef]
Anna, A. N., Priyana, Y., Rudiyanto, & Fikriyah, V. N. (2023). Water resources management model based area (A case study in some areas of Bengawan Solo watershed). Proceeding Of International Summit On Education, Tech-nology, And Humanity 2021, 2727(1), 050027. [Google Scholar]
Arnell, N. W., & Gosling, S. N. (2016). The impacts of climate change on river flood risk at the global scale. Climatic Change, 134(3), 387–401. doi: 10.1007/s10584-014-1084-5 [Google Scholar] [CrossRef]
Balac, M., & Hörl, S. (2021). Synthetic population for the state of California based on open-data: Examples of San Francisco Bay area and San Diego County. 100th Annual Meeting of the Transportation Research Board (TRB). Retrieved From https://hal.science/hal-03208848/
Bigi, F., Rashidi, T. H., & Viti, F. (2024). Synthetic Population: A Reliable Framework for Analysis for Agent-Based Modeling in Mobility. Transportation Research Record: Journal of the Transportation Research Board, 2678(11), 1–15. doi: 10.1177/03611981241239656 [Google Scholar] [CrossRef]
Burger, A., Oz, T., Crooks, A., & Kennedy, W. G. (2017). Generation of Realistic Mega-City Populations and Social Networks for Agent-Based Modeling. Proceedings of the 2017 International Conference of The Computatio-nal Social Science Society of the Americas, 1–7. doi: 10.1145/3145574.3145593 [Google Scholar] [CrossRef]
Chakraborty, L., Thistlethwaite, J., Scott, D., Henstra, D., Minano, A., & Rus, H. (2023). Assessing social vulnerability and identifying spatial hotspots of flood risk to inform socially just flood management policy. Risk Analysis, 43(5), 1058–1078. doi: 10.1111/risa.13978 [Google Scholar] [CrossRef]
Chapuis, K., & Taillandier, P. (2019). A brief review of synthetic population generation practices in agent-based social simulation. Submitted to SSC2019, Social Simulation Conference. Retrieved From https://www.researchgate.net/profile/Kevin-Chapuis/publication/335601121_A_brief_review_of_synthetic_population_generation_practices_in_agent-based_social_simulation/links/5d6f58e592851c1b3b279b9a/A-brief-review-of-synthetic-population-generation-practices-in-agent-based-social-simulation.pdf
Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social Vulnerability to Environmental Hazards*. Social Science Quarterly, 84(2), Article 2. doi: 10.1111/1540-6237.8402002 [Google Scholar] [CrossRef]
Cutter, S. L., & Finch, C. (2008). Temporal and spatial changes in social vulnerability to natural hazards. Proceedings of the National Academy of Sciences, 105(7), 2301–2306. doi: 10.1073/pnas.0710375105 [Google Scholar] [CrossRef]
De Mooij, J., Sonnenschein, T., Pellegrino, M., Dastani, M., Ettema, D., Logan, B., & Verstegen, J. A. (2024). Gen-SynthPop: Generating a spatially explicit synthetic population of individuals and households from aggregated data. Autonomous Agents and Multi-Agent Systems, 38(2), 48. doi: 10.1007/s10458-024-09680-7 [Google Scholar] [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), 2361813. doi: 10.1080/19475705.2024.2361813 [Google Scholar] [CrossRef]
Jiang, N., Crooks, A. T., Kavak, H., Burger, A., & Kennedy, W. G. (2022). A method to create a synthetic population with social networks for geographically-explicit agent-based models. Computational Urban Science, 2(1), 7. doi: 10.1007/s43762-022-00034-1 [Google Scholar] [CrossRef]
Jiang, N., Yin, F., Wang, B., & Crooks, A. T. (2024). A Large-Scale Geographically Explicit Synthetic Population with Social Networks for the United States. Scientific Data, 11(1), 1204. [Google Scholar]
Jumadi, J., Carver, S. J., & Quincey, D. J. (2019). An agent-based evaluation of varying evacuation scenarios in Merapi: Simultaneous and staged. Geosciences, 9(7), 317. [Google Scholar]
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: Journal of Geomatics and Planning, 11(2), 189–204. doi: 10.14710/geoplanning.11.2.189-204 [Google Scholar] [CrossRef]
Jumadi, J., Heppenstall, A., Malleson, N., Carver, S., Quincey, D., & Manville, V. (2018). Modelling individual evacua-tion decisions during natural disasters: A case study of volcanic crisis in Merapi, Indonesia. Geosciences, 8(6), Article 6. [Google Scholar]
Jumadi, J., Malleson, N., Carver, S., & Quincey, D. (2020). Estimating spatio-temporal risks from volcanic eruptions using an agent-based model. Journal of Artificial Societies and Social Simulation, 23(2) 2).
Jumadi, J., Sari, D. N., Umrotun, U., Musiyam, M., Nurmantyo, C., Muhammad, S. F., & Ibrahim, M. H. (2024). Remote Sensing and GIS-Driven Model for Flood Susceptibility Assessment in the Upper Solo River Watershed. Geo-graphia Technica, 19(2). [Google Scholar]
Jumadi, J., Danardono, D., Roziaty, E., Ulinuha, A., Supari, S., Choy, L. K., Sattar, F., & Nawaz, M. (2025). AI-Driven Ensemble Learning for Spatio-Temporal Rainfall Prediction in the Bengawan Solo River Watershed, Indonesia. Sustainability, 17(20), 9281. doi: 10.3390/su17209281 [Google Scholar] [CrossRef]
Li, Y., Gao, J., Yin, J., Liu, L., Zhang, C., & Wu, S. (2024). Flood Risk Assessment of Areas under Urbanization in Chongqing, China, by Integrating Multi-Models. Remote Sensing, 16(2), 2. doi: 10.3390/rs16020219 [Google Scholar] [CrossRef]
Maqtan, R., Othman, F., Jaafar, W. Z. W., & Elshafie, A. (2025). Flash flood data compilation and risk insights in Pe-ninsular Malaysia: a data-driven analysis (2014–2019). Natural Hazards, 1-27.
Musiyam, M., Jumadi, J., Wibowo, Y. A., Widiyatmoko, W., & Hafida, S. H. N. (2020). Analysis of Flood-Affected Areas Due to Extreme Weather In Pacitan, Indonesia. Geomate Journal, 19(75), 27–34. [Google Scholar]
Nejad, M. M., Erdogan, S., & Cirillo, C. (2021). A statistical approach to small area synthetic population generation as a basis for carless evacuation planning. Journal of Transport Geography, 90, 102902. [Google Scholar]
Pesaresi, M., Schiavina, M., Politis, P., Freire, S., Krasnodębska, K., Uhl, J. H., Carioli, A., Corbane, C., Dijkstra, L., Florio, P., Friedrich, H. K., Gao, J., Leyk, S., Lu, L., Maffenini, L., Mari-Rivero, I., Melchiorri, M., Syrris, V., Van Den Hoek, J., & Kemper, T. (2024). Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data. International Journal of Digital Earth, 17(1), 2390454. doi: 10.1080/17538947.2024.2390454 [Google Scholar] [CrossRef]
Prédhumeau, M., & Manley, E. (2023). A synthetic population for agent-based modelling in Canada. Scientific Data, 10(1), 148. [Google Scholar]
Purwanto, A., Andrasmoro, D., Eviliyanto, E., Rustam, R., Ibrahim, M. H., & Rohman, A. (2023). Validating the GIS-based flood susceptibility model using Synthetic Aperture Radar (SAR) data in sengah Temila watershed, Lan-dak Regency, Indonesia. Forum Geografi, 36(2), 185–201. doi: 10.23917/forgeo.v36i2.16368 [Google Scholar] [CrossRef]
Rubinyi, S., Verschuur, J., Goldblatt, R., Gussenbauer, J., Kowarik, A., Mannix, J., Bottoms, B., & Hall, J. (2022). High-resolution synthetic population mapping for quantifying disparities in disaster impacts: An application in the Bangladesh Coastal Zone. Frontiers in Environmental Science, 10, 1033579. [Google Scholar]
Sahid, S. (2024). Enhancing Digital Elevation Model Accuracy for Flood Modelling–A Case Study of the Ciberes River in Cirebon, Indonesia. Forum Geografi, 38(1), 40–56. doi: 10.23917/forgeo.v38i1.1839 [Google Scholar] [CrossRef]
Santhyami, S., Jumadi, J., Priyono, K. D., Rahayu, T., Sari, D. N., Othman, M., & Rudi, R. (2025). Spatio-Temporal Mo-deling for the Analysis of Hydrological Drought and its Impact on Rice Production in the Upper Bengawan Solo Basin, Central Java, Indonesia. Geographia Technica, 20(2). [Google Scholar]
Sawangnate, C., Chaisri, B., & Kittipongvises, S. (2022). Flood hazard mapping and flood preparedness literacy of the elderly population residing in Bangkok, Thailand. Water, 14(8), 1268. [Google Scholar]
Schiavina, M., Freire, S., & MacManus, K. (2023). GHS-POP R2023A-GHS population grid multitemporal (1975–2030). Joint Research Centre Data Catalogue.
Shu, E. G., Porter, J. R., Hauer, M. E., Sandoval Olascoaga, S., Gourevitch, J., Wilson, B., Pope, M., Melecio-Vazquez, D., & Kearns, E. (2023). Integrating climate change induced flood risk into future population projections. Na-ture Communications, 14(1), 7870. [Google Scholar]
Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., layback, D. A. (2021). Satellite imaging reveals increased proportion of population exposed to floods. Nature, 596(7870), 80-86. doi: 10.1038/s41586-021-03695-w [Google Scholar] [CrossRef]
Usamah, M., Handmer, J., Mitchell, D., & Ahmed, I. (2014). Can the vulnerable be resilient? Co-existence of vulnerabi-lity and disaster resilience: Informal settlements in the Philippines. International journal of disaster risk re-duction, 10, 178-189. [Google Scholar]
Waiyasusri, K., Wetchayont, P., Tananonchai, A., & Suwanmajo, D. (2023). Flood susceptibility mapping using logistic regression analysis in Lam Khan Chu watershed, Chaiyaphum Province, Thailand. Geography, Environment, Sustainability, 16(2), 41-56. [Google Scholar]
Waiyasusri, K., Kulpanich, N., Worachairungreung, M., Sae-Ngow, P., & Chaysmithikul, P. (2021). Flood Prone Risk Area Analysis During 2005-2019 In Lam Se Bok Watershed, Ubon Ratchathani Province, Thailand. Geogra-phia Technica, 16(1). [Google Scholar]
Zhang, M., Fu, X., Liu, S., & Zhang, C. (2025). Integrating Remote Sensing and Machine Learning for Actionable Flood Risk Assessment: Multi-Scenario Projection in the Ili River Basin in China Under Climate Change. Remote Sen-sing, 17(7), 1189. [Google Scholar]
Zhang, Q., Li, C., Wen, D., Kang, J., Chen, T., Zhang, B., Hu, Y., Yin, J., & Slater, L. (2025). Global South shows higher urban flood exposures than the Global North under current and future scenarios. Communications Earth & En-vironment, 6(1), 594. [Google Scholar]
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Muhammad Musiyam, Jumadi Jumadi, Vidya Nahdhiyatul Fikriyah, Heni Masruroh, Ema Dwi Septiyani, Choirul Amin, Hamim Zaky Hadibasyir, Farha Sattar, Muhammad Nawaz
This work is licensed under a Creative Commons Attribution 4.0 International License.
