Abstract
Flash floods continue to emerge as a serious and growing natural hazard for many communities worldwide, especially in areas affected by tropical storms. These floods damage critical infrastructure and severely strain economic resources, underscoring the urgent need for advanced flood prediction tools. This study presents an innovative integrated machine learning approach, BCMO-RF, which merges Balancing Composite Motion Optimization (BCMO) with Random Forest (RF) to map flash flood susceptibility. In the BCMO-RF approach, the RF algorithm is applied to develop the flash flood model, while BCMO is used to explore and optimize the model's parameters. The study concentrates on areas in Thanh Hoa Province, Vietnam, frequently impacted by flash floods. Accordingly, various geospatial data sources were utilized to compile a geodatabase comprising 2,540 flash flood locations and 12 influencing factors. The geodatabase served as the basis for training and validating the BCMO-RF model. Results show that the BCMO-RF model attained high prediction accuracy (93.7%), achieving a Kappa coefficient of 0.874 and an AUC score of 0.988, outperforming the Deep Learning model benchmark. The study finds that the BCMO-RF model is reliable for accurately mapping areas susceptible to flash floods.
Published Version
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