Assessing Urban Flood Risk Using LiDAR and GIS in Metropolitan City X

Thesis Abstract

Urban flood risk poses a significant threat to the socio-economic stability and sustainable development of Metropolitan City X, necessitating precise and efficient risk assessment methodologies. This study aims to evaluate flood susceptibility within the city by integrating high-resolution Light Detection and Ranging (LiDAR) datasets with Geographic Information Systems (GIS) platforms to enhance spatial analysis accuracy and predictive capabilities. The specific objectives include (1) to generate detailed digital elevation models (DEMs) and flood inundation maps leveraging LiDAR data; (2) to identify and spatially model urban flood susceptibility zones considering topographical, infrastructural, and land-use variables; and (3) to develop a predictive flood risk model that incorporates environmental and anthropogenic factors in the urban context. Employing a quantitative research design, the study utilizes a stratified random sampling approach to select 150 flood-prone zones within the metropolitan region based on historical flood data and socio-economic vulnerability indices. Data collection involved acquiring LiDAR datasets from the National Geospatial Agency, complemented by secondary data sources including meteorological records, urban infrastructure maps, and socio-economic surveys. Field validation was conducted through targeted ground-truthing and photographic documentation across 20 key flood sites. Data analysis employed a combination of geospatial techniques, including raster analysis, hydrological modeling, and spatial autocorrelation through Moran’s I. Advanced statistical methods such as logistic regression analysis were used to identify significant predictors of flood risk, with the model’s performance evaluated via Akaike Information Criterion (AIC) and Receiver Operating Characteristic (ROC) curve analysis. The study also employs GIS-based multicriteria decision analysis (MCDA) framework to delineate flood hazard zones, integrating physical, environmental, and human factors. The anticipated findings are expected to reveal critical topographical and infrastructural determinants influencing flood vulnerability, with the flood susceptibility maps demonstrating high accuracy in predicting flood-prone areas, as indicated by an expected ROC score exceeding 0.85. The study is grounded in the theoretical framework of the Hydrological Risk Theory and the Urban Resilience Theory, which justify the integration of physical data with socio-economic variables for holistic flood risk assessment. The expected contributions to knowledge include advancing methodological approaches by demonstrating the efficacy of LiDAR-GIS integration for urban flood modeling, providing a replicable framework for other urban centers in flood-prone regions, and informing policymakers and urban planners on targeted flood mitigation strategies. The main conclusion underscores the importance of high-resolution topographic data and integrated spatial analysis in enhancing urban flood management. It recommends the adoption of LiDAR-based flood modeling techniques for urban planning, the implementation of targeted infrastructure investments in identified high-risk zones, and the development of adaptive flood risk mitigation policies that incorporate spatial and socio-economic dynamics. Directions for future research include exploring climate change impacts on flood patterns and integrating real-time remote sensing data for dynamic flood monitoring. Overall, this study aims to contribute a scientifically robust, technologically advanced, and policy-relevant approach to urban flood risk assessment that supports sustainable urban development initiatives in Metropolitan City X.