Publisher

UMSUMS

Journal Forum GeografiForum Geografi Abstract

This study develops a spatial synthetic population (SSP)-based computational model to produce realistic, high-resolution flood-risk maps for the Upper Bengawan Solo Watershed. It combines Global Human 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, education, 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 suitability 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.

Conclusion

The study successfully developed a Spatial Synthetic Population (SSP) for flood risk analysis in the Upper Bengawan Solo Watershed, demonstrating strong validation in both distributional and spatial aspects.The SSP accurately represents demographic structures, with high agreement for age and gender, and a good spatial alignment with built-up areas.The resulting flood risk maps identify hotspots along river corridors and in urban centers, providing valuable information for targeted mitigation and preparedness efforts.

Future Research

Future research should explore the integration of dynamic population modeling, incorporating migration patterns and demographic changes, to enhance the long-term accuracy of flood risk assessments. Furthermore, investigating the impact of climate change scenarios on both flood hazards and population vulnerability is crucial for proactive adaptation planning. Finally, developing a user-friendly platform that allows local stakeholders to interact with the SSP and risk maps, enabling them to conduct what-if scenarios and prioritize interventions based on local context, would significantly enhance the practical application of this research. These advancements will require interdisciplinary collaboration, combining expertise in hydrology, demography, GIS, and community engagement to create more resilient and sustainable flood management strategies. The platform could also incorporate real-time data feeds, such as rainfall intensity and river levels, to provide dynamic risk assessments and improve early warning systems, ultimately empowering communities to better prepare for and respond to flood events. This holistic approach will contribute to a more comprehensive understanding of flood risk and facilitate the development of effective mitigation measures tailored to the specific needs of the Upper Bengawan Solo Watershed.

References

1. Flood Risk Analysis Using Spatial Synthetic Population in the Upper Bengawan Solo Watershed, Indonesia... journals2.ums.ac.id/fg/article/view/13611Flood Risk Analysis Using Spatial Synthetic Population in the Upper Bengawan Solo Watershed Indonesia journals2 ums ac fg article view 13611
2. OSF. osf osf.io/2kejqOSF osf osf io 2kejq

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File size	1.87 MB
Pages	15
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