Assessing Urban Flood Risk Using GIS and Remote Sensing in City Council Planning

Thesis Abstract

Urban flooding poses a significant threat to socio-economic stability and environmental sustainability in rapidly expanding cities, especially in regions experiencing increased stormwater intensity and inadequate drainage infrastructure. This study aims to provide a comprehensive assessment of urban flood risk through the integration of Geographic Information Systems (GIS) and remote sensing technologies to support evidence-based City Council planning and disaster risk reduction strategies. The specific objectives include (1) mapping flood-prone zones using high-resolution satellite imagery and digital elevation models; (2) analyzing land use/land cover changes influencing flood vulnerability; (3) developing a flood risk index that combines environmental, infrastructural, and climate data; and (4) evaluating current urban flood management practices for policy enhancement. The research adopts a mixed-methods approach within a descriptive and analytical design framework. The population comprises urban land parcels, drainage infrastructure data, and historical flood records within the city limits, with a sample selection of 500 land parcels and 50 drainage infrastructure sites obtained through stratified random sampling. Primary data sources include satellite imagery from Sentinel-2 and Landsat 8 (spanning the last ten years), topographical maps, and municipal flood incident reports. Data collection instruments encompass GIS software for spatial analysis, remote sensing image processing tools, and structured interview guides for key city officials involved in flood management. Ensuring data validity involves calibration of remote sensing data against ground-truth surveys and cross-verification of flood incident reports. The reliability of survey instruments is piloted before deployment. Data analysis employs supervised classification of satellite imagery for land cover change detection, hydrological modeling to delineate flood-prone areas, and spatial overlay analysis using ArcGIS and QGIS platforms. The flood risk index is computed via a weighted scoring model incorporating factors such as proximity to waterways, surface run-off potential derived from elevation data, and urbanization density. Advanced statistical techniques including multiple regression analysis will quantify the relationship between environmental variables and flood incidences, while factor analysis will identify key contributors to flood vulnerability. Qualitative data from interviews are subjected to thematic analysis to assess policy implementation gaps. Ethical considerations include obtaining relevant permissions, anonymization of data, and adherence to confidentiality protocols. Expected findings include detailed flood hazard maps highlighting high-risk zones, identification of critical infrastructural vulnerabilities, and a validated flood risk index that can inform targeted interventions. The analysis is anticipated to reveal significant correlations between urban land cover changes, surface runoff, and flood occurrences, underpinning the importance of integrated spatial planning. The study will demonstrate how GIS and remote sensing effectively facilitate real-time flood risk assessment, offering a replicable model for other urban centers. This research contributes to the theoretical understanding of urban flood dynamics by applying and testing the Flood Vulnerability Framework and the Theory of Urban Resilience. Practically, the study offers a robust spatial planning tool that incorporates environmental and infrastructural factors to improve flood mitigation strategies. The findings underscore the importance of integrating geospatial data into urban planning regimes, providing policymakers with a scientific basis for zoning, drainage enhancement, and emergency preparedness. Ultimately, the study concludes that employing GIS and remote sensing significantly enhances the accuracy and efficiency of flood risk assessment processes. Recommendations include adopting a GIS-based flood monitoring system, revising land use policies to limit urban expansion in high-risk zones, and investing in resilient drainage infrastructure. The research advocates for institutionalizing geospatial technologies within city planning departments and suggests avenues for future research focusing on the application of machine learning algorithms for real-time hazard prediction. This work advances the state of knowledge in urban flood risk management and framework design, thereby fostering sustainable urban development practices in flood-prone contexts.