LEVERAGING GEOSPATIAL INFORMATION SYSTEMS FOR PREDICTIVE FLOOD MODELING AND EVIDENCE-DRIVEN DISASTER RISK REDUCTION POLICY DEVELOPMENT

Flooding is among the most frequent and costly natural hazards worldwide, with its impacts increasingly
intensified by climate change, land-use change, and expanding human settlements. In the United States, recurrent
flood disasters have revealed persistent gaps in predictive capacity, infrastructure preparedness, and the translation
of scientific risk assessments into effective disaster risk reduction (DRR) policies. Addressing these challenges
requires analytical frameworks that not only anticipate flood behavior but also support evidence-driven decisionmaking across planning and governance scales. Geospatial Information Systems (GIS) play a critical role in this
process by enabling the integration, analysis, and visualization of spatially explicit flood risk data within a
predictive modeling environment. This study explores the use of GIS-based predictive flood modeling to support
evidence-driven DRR policy development. At a broad level, the framework integrates hydrological, topographic,
climatic, and land-use datasets to simulate flood susceptibility and forecast potential inundation under varying
rainfall and runoff scenarios. Spatial analytics techniques including terrain analysis, hydrological modeling,
spatial regression, and scenario-based simulation are applied to identify evolving flood risk patterns and quantify
uncertainty in hazard projections. The framework is demonstrated through case studies from flood-prone regions
in the United States, illustrating how predictive GIS models can inform policy-relevant insights at local, regional,
and state levels. The results show how spatially explicit flood forecasts can guide infrastructure investment
prioritization, land-use regulation, and emergency preparedness strategies. In particular, the study highlights the
value of GIS outputs in aligning predictive flood intelligence with disaster mitigation planning, zoning decisions,
and early warning system design. Beyond operational applications, the research emphasizes the role of GISenabled predictive modeling in strengthening DRR policy formulation by improving transparency, supporting
interagency coordination, and enabling adaptive governance under uncertainty. The findings demonstrate that
GIS-based predictive flood modeling provides a robust foundation for transforming flood risk science into
actionable, evidence-driven disaster risk reduction policies across diverse U.S. contexts