Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall
Urbanization has increased impervious surfaces, while climate change has intensified rainfall, leading to more frequent urban flooding. Traditional numerical models for flood prediction are accurate but time-consuming due to extensive parameter calibration and data processing. This study addresses t...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Progress in Disaster Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590061725000122 |
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| author | Se-Dong Jang Jae-Hwan Yoo Yeon-Su Lee Byunghyun Kim |
| author_facet | Se-Dong Jang Jae-Hwan Yoo Yeon-Su Lee Byunghyun Kim |
| author_sort | Se-Dong Jang |
| collection | DOAJ |
| description | Urbanization has increased impervious surfaces, while climate change has intensified rainfall, leading to more frequent urban flooding. Traditional numerical models for flood prediction are accurate but time-consuming due to extensive parameter calibration and data processing. This study addresses these limitations by proposing a machine learning-based flood prediction method using a Random Forest model. By utilizing past rainfall data, 1D drainage system simulations, and 2D flood analyses, we trained the model to predict flood patterns for various rainfall events. To enhance prediction accuracy, statistical characteristics of rainfall, such as temporal distribution, were incorporated into the model. Performance metrics (RMSE, R2, MAE) for the test dataset showed values of 3.1573, 0.9682, and 0.9484 for the total rainfall model, and 2.7354, 0.9761, and 0.8942 for the model with statistical characteristics. Both models displayed high predictive accuracy relative to the numerical model, with the Random Forest model using statistical characteristics showing slightly improved performance. This method provides faster, reliable flood predictions, offering a valuable tool for real-time urban flood management and decision-making during emergency situations. |
| format | Article |
| id | doaj-art-1b2106a5820f467bb1172ea5dbf216ee |
| institution | OA Journals |
| issn | 2590-0617 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Progress in Disaster Science |
| spelling | doaj-art-1b2106a5820f467bb1172ea5dbf216ee2025-08-20T02:06:15ZengElsevierProgress in Disaster Science2590-06172025-04-012610041510.1016/j.pdisas.2025.100415Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfallSe-Dong Jang0Jae-Hwan Yoo1Yeon-Su Lee2Byunghyun Kim3Department of Civil Engineering, Kyungpook National University, Daegu 41566, Republic of KoreaDepartment of Civil Engineering, Kyungpook National University, Daegu 41566, Republic of KoreaDepartment of Civil Engineering, Kyungpook National University, Daegu 41566, Republic of KoreaCorresponding author.; Department of Civil Engineering, Kyungpook National University, Daegu 41566, Republic of KoreaUrbanization has increased impervious surfaces, while climate change has intensified rainfall, leading to more frequent urban flooding. Traditional numerical models for flood prediction are accurate but time-consuming due to extensive parameter calibration and data processing. This study addresses these limitations by proposing a machine learning-based flood prediction method using a Random Forest model. By utilizing past rainfall data, 1D drainage system simulations, and 2D flood analyses, we trained the model to predict flood patterns for various rainfall events. To enhance prediction accuracy, statistical characteristics of rainfall, such as temporal distribution, were incorporated into the model. Performance metrics (RMSE, R2, MAE) for the test dataset showed values of 3.1573, 0.9682, and 0.9484 for the total rainfall model, and 2.7354, 0.9761, and 0.8942 for the model with statistical characteristics. Both models displayed high predictive accuracy relative to the numerical model, with the Random Forest model using statistical characteristics showing slightly improved performance. This method provides faster, reliable flood predictions, offering a valuable tool for real-time urban flood management and decision-making during emergency situations.http://www.sciencedirect.com/science/article/pii/S2590061725000122Urban floodFlood forecastingMachine learningRandom forestStatistical characteristic of rainfall |
| spellingShingle | Se-Dong Jang Jae-Hwan Yoo Yeon-Su Lee Byunghyun Kim Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall Progress in Disaster Science Urban flood Flood forecasting Machine learning Random forest Statistical characteristic of rainfall |
| title | Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall |
| title_full | Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall |
| title_fullStr | Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall |
| title_full_unstemmed | Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall |
| title_short | Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall |
| title_sort | flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall |
| topic | Urban flood Flood forecasting Machine learning Random forest Statistical characteristic of rainfall |
| url | http://www.sciencedirect.com/science/article/pii/S2590061725000122 |
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