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

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Bibliographic Details
Title: Automating GIS-Based Cloudburst Risk Mapping Using Generative AI: A Framework for Scalable Hydrological Analysis.
Authors: Adiyasa, Alexander, Mantegna, Andrea Niccolò, Kveladze, Irma
Source: Hydrology (2306-5338); Aug2025, Vol. 12 Issue 8, p196, 11p
Subject Terms: GENERATIVE artificial intelligence, FLOOD risk, HYDROLOGICAL research, HUMAN-artificial intelligence interaction, GEOGRAPHIC information systems, RISK assessment, DECISION support systems, RAINSTORMS
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Biomedical Index
Description
Abstract: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 km<sup>2</sup> 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. [ABSTRACT FROM AUTHOR]
ISSN:23065338
DOI:10.3390/hydrology12080196