Automating GIS-Based Cloudburst Risk Mapping Using Generative AI: A Framework for Scalable Hydrological Analysis

• Published in: Hydrology Vol. 12; no. 8; p. 196
• Main Authors: Adiyasa, Alexander, Mantegna, Andrea Niccolò, Kveladze, Irma
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Automation; Classification; cloudburst; Cloudbursts; code generation; Cognition & reasoning; Decision making; Decision support systems; Denmark; Drainage; Efficiency; Embedding; Environmental aspects; Environmental risk; Floods; Flow paths; Flow velocity; Generative artificial intelligence; Geographic information systems; Geographical information systems; GIS automation; Hydrologic models; Hydrological analysis; hydrological modeling; Hydrology; Land use; Land use management; Land use planning; large language models; Libraries; Machine learning; Methods; Natural language; Planning; Precipitation (Meteorology); Python; Risk assessment; Rivers; Software; Software development; Subject specialists; Topography; Denmark
• ISSN: 2306-5338; 2306-5338
• DOI: 10.3390/hydrology12080196

Accurate dynamic hydrological models are often too complex and costly for the rapid, broad-scale screening necessitated for proactive land-use planning against increasing cloudburst risks. This paper demonstrates the use of GPT-4 to develop a GUI-based Python 3.13.2 application for geospatial flood risk assessments. The study used instructive prompt techniques to script a traditional stream and catchment delineation methodology, further embedding it with a custom GUI. The resulting application demonstrates high performance, processing a 29.63 km2 catchment at a 1 m resolution in 30.31 s, and successfully identifying the main upstream contributing areas and flow paths for a specified area of interest. While its accuracy is limited by terrain data artifacts causing stream breaks, this study demonstrates how human–AI collaboration, with the LLM acting as a coding assistant guided by domain expertise, can empower domain experts and facilitate the development of advanced GIS-based decision-support systems.