Open Access

ARTICLE

AI-Driven GIS Modeling of Future Flood Risk and Susceptibility for Typhoon Krathon under Climate Change

Chih-Yu Liu^1,2, Cheng-Yu Ku^1,2,*, Ming-Han Tsai¹, Jia-Yi You³

1 Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung, 202301, Taiwan
2 Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung, 202301, Taiwan
3 Keelung City Fire Department, Keelung City Government, Keelung, 204004, Taiwan

* Corresponding Author: Cheng-Yu Ku. Email:

Computer Modeling in Engineering & Sciences 2025, 144(3), 2969-2990. https://doi.org/10.32604/cmes.2025.070663

Received 21 July 2025; Accepted 05 September 2025; Issue published 30 September 2025

Abstract

Amid growing typhoon risks driven by climate change with projected shifts in precipitation intensity and temperature patterns, Taiwan faces increasing challenges in flood risk. In response, this study proposes a geographic information system (GIS)-based artificial intelligence (AI) model to assess flood susceptibility in Keelung City, integrating geospatial and hydrometeorological data collected during Typhoon Krathon (2024). The model employs the random forest (RF) algorithm, using seven environmental variables excluding average elevation, slope, topographic wetness index (TWI), frequency of cumulative rainfall threshold exceedance, normalized difference vegetation index (NDVI), flow accumulation, and drainage density, with the number of flood events per unit area as the output. The RF model demonstrates high accuracy, achieving the accuracy of 97.45%. Feature importance indicates that NDVI is the most critical predictor, followed by flow accumulation, TWI, and rainfall frequency. Furthermore, under the IPCC AR5 RCP8.5 scenarios, projected 50-year return period rainfall in Keelung City increases by 42.40%–64.95% under +2°C to +4°C warming. These projections were integrated into the RF model to simulate future flood susceptibility. Results indicate two districts in the study area face the greatest increase in flood risk, emphasizing the need for targeted climate adaptation in vulnerable urban areas.

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Keywords

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