A Comprehensive Framework for Urban Flood Risk Assessment Using Integrated GIS and Remote Sensing Data

Thesis Abstract

Rapid urbanization has intensified the frequency and severity of urban flooding, posing significant risks to life, infrastructure, and economic stability. Existing approaches to flood risk assessment often lack integration of multiple geospatial data sources, resulting in incomplete or inaccurate evaluations. This study aims to develop a comprehensive framework for urban flood risk assessment through the integration of Geographic Information Systems (GIS) and remote sensing data, thereby enhancing predictive accuracy and supporting strategic urban planning. The specific objectives include (1) identifying key environmental, infrastructural, and socio-economic variables influencing flood susceptibility; (2) evaluating the effectiveness of multi-source geospatial data integration methods; (3) designing a spatially explicit flood risk assessment model; and (4) validating the framework with empirical data from a major metropolitan area experiencing recurrent flooding issues. The methodology adopts a mixed-method research design, combining quantitative spatial analysis with qualitative expert validation. The population for this study comprises urban areas with documented flood incidences, with a focus on a sample of 10 neighborhoods selected based on flood history, socio-economic diversity, and land use patterns. A sample size of 150 households was determined using stratified random sampling to capture local vulnerability perceptions. Data collection instruments include high-resolution satellite imagery from Sentinel-2 and Landsat 8, digital elevation models (DEMs), municipal infrastructure GIS layers, and socio-economic surveys administered through structured questionnaires. The validity and reliability of the survey instruments are established through pilot testing and Cronbach’s alpha coefficients exceeding 0.80. Geospatial data is processed and analyzed using ArcGIS Pro and ENVI software, employing raster and vector analysis techniques. The integration of remote sensing and GIS data is achieved via the framework of the Analytic Hierarchy Process (AHP) to weight different vulnerability factors, supported by principal component analysis (PCA) to reduce dimensionality. Spatial modeling utilizes logistic regression to quantify flood susceptibility based on environmental and infrastructural variables. The model’s performance is assessed through receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics. Qualitative validation involves thematic analysis of flood management practices and community perceptions obtained through semi-structured interviews with 20 local officials and residents. Ethical considerations adhere to research standards for informed consent, confidentiality, and data security. Anticipated findings include a robust spatial model delineating flood-prone zones with high predictive accuracy, supported by significant statistical relationships identified through regression analysis. The integrated framework is expected to demonstrate superior performance over univariate or traditional assessments, offering finer spatial resolution of vulnerability hotspots. The study also aims to uncover socio-economic and infrastructural factors that exacerbate flood risks, providing a comprehensive understanding of urban flood dynamics. This research contributes to existing knowledge by operationalizing an innovative, multisource geospatial integration framework that enhances flood risk assessment applications. It extends theoretical understanding of spatial vulnerability modeling by applying and validating the Risk Matrix Theory and the Social-Ecological Systems Theory within an urban flood context. The practical implications include informing urban planners, policymakers, and emergency managers in designing targeted interventions, land use policies, and community-based flood resilience strategies. The study concludes that integrated GIS and remote sensing approaches significantly improve the accuracy and utility of urban flood risk assessments. Recommendations emphasize adopting the proposed framework for routine flood monitoring and integrating it into urban disaster preparedness plans. Future research should explore dynamic modeling incorporating climate change projections and real-time sensor data to further refine flood risk forecasting and response capabilities.