Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction
Regarding urban flooding issues, applying Artificial Intelligence (AI) methodologies can provide a timely prediction of imminent incidences of flash floods. The study aims to develop and deploy an effective real-time pluvial flood forecasting AI platform. The platform integrates rainfall hyetographs...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/12/3552 |
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| author | Deng-Lin Chang Sheng-Hsueh Yang Sheau-Ling Hsieh Hui-Jung Wang Keh-Chia Yeh |
| author_facet | Deng-Lin Chang Sheng-Hsueh Yang Sheau-Ling Hsieh Hui-Jung Wang Keh-Chia Yeh |
| author_sort | Deng-Lin Chang |
| collection | DOAJ |
| description | Regarding urban flooding issues, applying Artificial Intelligence (AI) methodologies can provide a timely prediction of imminent incidences of flash floods. The study aims to develop and deploy an effective real-time pluvial flood forecasting AI platform. The platform integrates rainfall hyetographs embedded with uncertainty analyses as well as hydrological and hydraulic modeling. It establishes a large number synthetic of torrential rainfall events and their simulated flooding datasets. The obtained data contain 6000 sets of color-classified rainfall hyetograph maps and 300,000 simulated flooding maps (water depth) in an urban district. The generated datasets are utilized for AI image processing. Through the AI deep learning classifications, the rainfall hyetograph map feature parameters are detected and extracted. The trained features are applied to predict potential rainfall events, recognize their potential inundated water depths as well as display flooding maps in real-time. The performance assessments of the platform are evaluated by Root Means Square Error (RMSE), Nash Sutcliffe Efficiency Coefficient (NSCE) and Mean Absolute Percentage Error (MAPE). The results of RMSE and NSCE indicators illustrate that the methodologies and approaches of the AI platform are reliable and acceptable. However, the values of MAPE show inconsistency. Ultimately, the platform can perform and be utilized promptly in real-time and ensure sufficient lead time in order to prevent possible flooding hazards. |
| first_indexed | 2024-03-10T13:59:21Z |
| format | Article |
| id | doaj.art-af0728dd990d4d00a6d99c3b924f9265 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T13:59:21Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-af0728dd990d4d00a6d99c3b924f92652023-11-21T01:21:16ZengMDPI AGWater2073-44412020-12-011212355210.3390/w12123552Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and PredictionDeng-Lin Chang0Sheng-Hsueh Yang1Sheau-Ling Hsieh2Hui-Jung Wang3Keh-Chia Yeh4Department of Civil Engineering, National Chiao Tung University, Hsinchu 30010, TaiwanDisaster Prevention & Water Environment Research Center, National Chiao Tung University, Hsinchu 30010, TaiwanDisaster Prevention & Water Environment Research Center, National Chiao Tung University, Hsinchu 30010, TaiwanDisaster Prevention & Water Environment Research Center, National Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Civil Engineering, National Chiao Tung University, Hsinchu 30010, TaiwanRegarding urban flooding issues, applying Artificial Intelligence (AI) methodologies can provide a timely prediction of imminent incidences of flash floods. The study aims to develop and deploy an effective real-time pluvial flood forecasting AI platform. The platform integrates rainfall hyetographs embedded with uncertainty analyses as well as hydrological and hydraulic modeling. It establishes a large number synthetic of torrential rainfall events and their simulated flooding datasets. The obtained data contain 6000 sets of color-classified rainfall hyetograph maps and 300,000 simulated flooding maps (water depth) in an urban district. The generated datasets are utilized for AI image processing. Through the AI deep learning classifications, the rainfall hyetograph map feature parameters are detected and extracted. The trained features are applied to predict potential rainfall events, recognize their potential inundated water depths as well as display flooding maps in real-time. The performance assessments of the platform are evaluated by Root Means Square Error (RMSE), Nash Sutcliffe Efficiency Coefficient (NSCE) and Mean Absolute Percentage Error (MAPE). The results of RMSE and NSCE indicators illustrate that the methodologies and approaches of the AI platform are reliable and acceptable. However, the values of MAPE show inconsistency. Ultimately, the platform can perform and be utilized promptly in real-time and ensure sufficient lead time in order to prevent possible flooding hazards.https://www.mdpi.com/2073-4441/12/12/3552artificial intelligencedeep learningurban floodingflash floodshyetograph mapreal-time flooding map datasets |
| spellingShingle | Deng-Lin Chang Sheng-Hsueh Yang Sheau-Ling Hsieh Hui-Jung Wang Keh-Chia Yeh Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction Water artificial intelligence deep learning urban flooding flash floods hyetograph map real-time flooding map datasets |
| title | Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction |
| title_full | Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction |
| title_fullStr | Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction |
| title_full_unstemmed | Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction |
| title_short | Artificial Intelligence Methodologies Applied to Prompt Pluvial Flood Estimation and Prediction |
| title_sort | artificial intelligence methodologies applied to prompt pluvial flood estimation and prediction |
| topic | artificial intelligence deep learning urban flooding flash floods hyetograph map real-time flooding map datasets |
| url | https://www.mdpi.com/2073-4441/12/12/3552 |
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