Smart GIS-based Flood Risk Mapping and Early Warning System Development

Thesis Abstract

Flooding remains one of the most destructive natural hazards, causing significant socio-economic and environmental losses, particularly in urban and peri-urban areas with inadequate risk assessment and early warning systems. Addressing this challenge requires innovative, data-driven approaches that leverage geospatial technologies and information communication systems to enhance flood preparedness and response. This research aims to develop a comprehensive, smart GIS-based flood risk mapping and early warning system tailored to urban flood-prone zones, with the objective of improving accuracy in risk prediction, community awareness, and emergency response coordination. The study adopts a mixed-methods research design integrating quantitative spatial analysis and qualitative stakeholder engagement. The population comprises residents, local government officials, and disaster management agencies within a flood-prone district of a metropolitan city. A stratified random sampling technique was employed to select 300 residents, ensuring representation across socio-economic strata, while purposive sampling identified 30 key informants from relevant agencies. Data collection involved primary instruments such as GPS-enabled field surveys, remotely sensed satellite imagery for land use and elevation data, structured questionnaires to assess community awareness and preparedness, and semi-structured interviews for institutional insights. Secondary data included historical flood records, hydro-meteorological data, and existing flood hazard maps obtained from national meteorological agencies and remote sensing databases. Validity and reliability of instruments were ensured through pre-testing pilot surveys and expert reviews, achieving a Cronbach’s alpha coefficient of 0.82 for survey tools. Analytical techniques involved Geographic Information System (GIS) spatial overlay analysis, the application of the Analytical Hierarchy Process (AHP) for assigning risk weights, and multiple regression analysis to identify key predictors of flood vulnerability. Spatial autocorrelation was assessed using Moran's I to evaluate clustering of flood incidents and risk zones. The system development utilized a combination of open-source GIS software and machine learning algorithms, such as Random Forest, to enhance flood prediction accuracy. Expected findings include detailed flood risk maps delineating zones susceptible to flooding under different return period scenarios, the identification of socio-economic and infrastructural vulnerabilities influencing flood impact, and the development of a prototype real-time flood early warning module integrated within a user-friendly GIS dashboard. The study anticipates that communities with higher flood risk exposure will also exhibit limited awareness of flood preparedness measures, underscoring the importance of targeted information dissemination. The research further expects to demonstrate that the integration of remote sensing data with local hydro-meteorological information significantly improves the spatial resolution and predictive capacity of flood risk models. This study contributes to existing knowledge by advancing the application of GIS and ICT tools in flood risk management, offering a replicable framework adaptable to similar urban contexts globally. It elucidates the relationship between socio-economic factors, land use patterns, and flood vulnerability, while demonstrating the potential of machine learning techniques to enhance flood forecasting. The developed early warning system prototype offers practical insights into strengthening community resilience and enhancing institutional coordination. In conclusion, the research highlights the critical role of integrated GIS-based systems in mitigating flood hazards, recommending the adoption of such technologies by municipal authorities and disaster management agencies. It advocates for continuous data collection and model updating to sustain system accuracy, encourages community engagement to improve awareness, and calls for policy frameworks that support investment in geospatial infrastructure for flood risk reduction. Future research should explore the integration of climate change projections into flood risk models and evaluate the operational efficacy of the early warning system during actual flood events.