Enhancing Flood Prediction in Urban Areas: A Machine Learning Approach for Makassar City DOI Open Access
Muhammad Rizal, Mochamad Hariadi, Yunifa Miftachul Arif

et al.

Engineering Technology & Applied Science Research, Journal Year: 2025, Volume and Issue: 15(2), P. 21678 - 21684

Published: April 3, 2025

Accurate and rapid predictions regarding urban flooding, are essential in supporting risk mitigation efforts. Flood phenomena have the potential to cause extensive damage disrupt functions of economic governmental sectors. However, these impacts can be minimized through comprehensive planning preparation reduce losses. Machine learning techniques emerged as a promising method for predicting complex hydrological processes. This research develops flood prediction model by comparing seven machine algorithms, namely Logistic Regression, Linear Discriminant Analysis, k-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machine, AdaBoost, Random Forest. The results show that Forest has highest performance, demonstrating reliability processing datasets. is expected enhance disaster contribute significantly management areas.

Language: Английский

Enhancing Flood Prediction in Urban Areas: A Machine Learning Approach for Makassar City DOI Open Access
Muhammad Rizal, Mochamad Hariadi, Yunifa Miftachul Arif

et al.

Engineering Technology & Applied Science Research, Journal Year: 2025, Volume and Issue: 15(2), P. 21678 - 21684

Published: April 3, 2025

Accurate and rapid predictions regarding urban flooding, are essential in supporting risk mitigation efforts. Flood phenomena have the potential to cause extensive damage disrupt functions of economic governmental sectors. However, these impacts can be minimized through comprehensive planning preparation reduce losses. Machine learning techniques emerged as a promising method for predicting complex hydrological processes. This research develops flood prediction model by comparing seven machine algorithms, namely Logistic Regression, Linear Discriminant Analysis, k-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machine, AdaBoost, Random Forest. The results show that Forest has highest performance, demonstrating reliability processing datasets. is expected enhance disaster contribute significantly management areas.

Language: Английский

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