Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition
In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/10/2/47 |
| _version_ | 1797620681832136704 |
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| author | Yen-Chang Chen Hui-Chung Yeh Su-Pai Kao Chiang Wei Pei-Yi Su |
| author_facet | Yen-Chang Chen Hui-Chung Yeh Su-Pai Kao Chiang Wei Pei-Yi Su |
| author_sort | Yen-Chang Chen |
| collection | DOAJ |
| description | In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level data. EEMD is used to decompose water level signals from a tidal river into several intrinsic mode functions (IMFs). These IMFs are then used to reconstruct the ocean and stream components that represent the tide and river flow, respectively. The forecasting model is obtained through stepwise regression on these components. The ocean component at a location 1 h ahead can be forecast using the observed ocean components at the downstream gauging stations, and the corresponding stream component can be forecast using the water stages at the upstream gauging stations. Summing these two forecasted components enables the forecasting of the water level at a location in the tidal river. The proposed model is conceptually simple and highly accurate. Water level data collected from gauging stations in the Tanshui River in Taiwan during typhoons were used to assess the feasibility of the proposed model. The water level forecasting model accurately and reliably predicted the water level at the Taipei Bridge gauging station. |
| first_indexed | 2024-03-11T08:45:07Z |
| format | Article |
| id | doaj.art-993fce22632642efb4bc3371997530bd |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-11T08:45:07Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj.art-993fce22632642efb4bc3371997530bd2023-11-16T20:51:47ZengMDPI AGHydrology2306-53382023-02-011024710.3390/hydrology10020047Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode DecompositionYen-Chang Chen0Hui-Chung Yeh1Su-Pai Kao2Chiang Wei3Pei-Yi Su4Department of Civil Engineering, National Taipei University of Technology, Taipei 10617, TaiwanDepartment of Land Resources, Chinese Culture University, Taipei 11114, TaiwanTYLIN International Taiwan, Taipei 10657, TaiwanExperimental Forest, National Taiwan University, Zhushan, Nantou 55750, TaiwanDepartment of Civil Engineering, National Taipei University of Technology, Taipei 10617, TaiwanIn this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model is that the only required data are water level data. EEMD is used to decompose water level signals from a tidal river into several intrinsic mode functions (IMFs). These IMFs are then used to reconstruct the ocean and stream components that represent the tide and river flow, respectively. The forecasting model is obtained through stepwise regression on these components. The ocean component at a location 1 h ahead can be forecast using the observed ocean components at the downstream gauging stations, and the corresponding stream component can be forecast using the water stages at the upstream gauging stations. Summing these two forecasted components enables the forecasting of the water level at a location in the tidal river. The proposed model is conceptually simple and highly accurate. Water level data collected from gauging stations in the Tanshui River in Taiwan during typhoons were used to assess the feasibility of the proposed model. The water level forecasting model accurately and reliably predicted the water level at the Taipei Bridge gauging station.https://www.mdpi.com/2306-5338/10/2/47ensemble empirical mode decomposition (EEMD)flood periodtidal riverwater level forecasting |
| spellingShingle | Yen-Chang Chen Hui-Chung Yeh Su-Pai Kao Chiang Wei Pei-Yi Su Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition Hydrology ensemble empirical mode decomposition (EEMD) flood period tidal river water level forecasting |
| title | Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition |
| title_full | Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition |
| title_fullStr | Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition |
| title_full_unstemmed | Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition |
| title_short | Water Level Forecasting in Tidal Rivers during Typhoon Periods through Ensemble Empirical Mode Decomposition |
| title_sort | water level forecasting in tidal rivers during typhoon periods through ensemble empirical mode decomposition |
| topic | ensemble empirical mode decomposition (EEMD) flood period tidal river water level forecasting |
| url | https://www.mdpi.com/2306-5338/10/2/47 |
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