WLP-VBL: A Robust Lightweight Model for Water Level Prediction
Accurate and reliable water level prediction plays a crucial role in the optimal management of water resources and reservoir scheduling. Water level data have the characteristics of volatility and temporality; a single water level prediction model can only be applied to specific hydrological conditi...
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| Format: | Article |
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MDPI AG
2023-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/19/4048 |
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| author | Congqin Yi Wenshu Huang Haiyan Pan Jinghan Dong |
| author_facet | Congqin Yi Wenshu Huang Haiyan Pan Jinghan Dong |
| author_sort | Congqin Yi |
| collection | DOAJ |
| description | Accurate and reliable water level prediction plays a crucial role in the optimal management of water resources and reservoir scheduling. Water level data have the characteristics of volatility and temporality; a single water level prediction model can only be applied to specific hydrological conditions and reservoirs. Therefore, in this paper, we present a robust lightweight model for water level prediction, namely WLP-VBL, by using a combination of VMD, BA, and LSTM. The proposed WLP-VBL model consists of three steps: first, the water level dataset is decomposed by EMD to obtain a number of decomposition layers K, and then VMD is used to decompose the original water level dataset into K intrinsic modal functions (IMFs) to produce a clearer signal. Next, the IMF data are sent to an LSTM neural network optimized by BA for prediction, and finally each component is superimposed to obtain the predicted value. In order to evaluate the effectiveness of the model, experiments were carried out on water level data for the Gan River. The results indicate that: (1) Compared with state-of-the art methods, e.g., LSTM, VMD-LSTM, and EMD-LSTM, WLP-VBL exhibited the best performance. The MSE and MAE of WLP-VBL decreased by 69.6~74.7% and 45~98.5%, respectively. (2) The proposed model showed stronger robustness for water level prediction, and was able to handle highly volatile and noisy data. |
| first_indexed | 2024-03-10T21:47:11Z |
| format | Article |
| id | doaj.art-ee41ed575880497a983771d5540ff41e |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:47:11Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ee41ed575880497a983771d5540ff41e2023-11-19T14:16:30ZengMDPI AGElectronics2079-92922023-09-011219404810.3390/electronics12194048WLP-VBL: A Robust Lightweight Model for Water Level PredictionCongqin Yi0Wenshu Huang1Haiyan Pan2Jinghan Dong3College of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaAccurate and reliable water level prediction plays a crucial role in the optimal management of water resources and reservoir scheduling. Water level data have the characteristics of volatility and temporality; a single water level prediction model can only be applied to specific hydrological conditions and reservoirs. Therefore, in this paper, we present a robust lightweight model for water level prediction, namely WLP-VBL, by using a combination of VMD, BA, and LSTM. The proposed WLP-VBL model consists of three steps: first, the water level dataset is decomposed by EMD to obtain a number of decomposition layers K, and then VMD is used to decompose the original water level dataset into K intrinsic modal functions (IMFs) to produce a clearer signal. Next, the IMF data are sent to an LSTM neural network optimized by BA for prediction, and finally each component is superimposed to obtain the predicted value. In order to evaluate the effectiveness of the model, experiments were carried out on water level data for the Gan River. The results indicate that: (1) Compared with state-of-the art methods, e.g., LSTM, VMD-LSTM, and EMD-LSTM, WLP-VBL exhibited the best performance. The MSE and MAE of WLP-VBL decreased by 69.6~74.7% and 45~98.5%, respectively. (2) The proposed model showed stronger robustness for water level prediction, and was able to handle highly volatile and noisy data.https://www.mdpi.com/2079-9292/12/19/4048water levelvariational-mode-decompositionbat algorithmlong short-term memory |
| spellingShingle | Congqin Yi Wenshu Huang Haiyan Pan Jinghan Dong WLP-VBL: A Robust Lightweight Model for Water Level Prediction Electronics water level variational-mode-decomposition bat algorithm long short-term memory |
| title | WLP-VBL: A Robust Lightweight Model for Water Level Prediction |
| title_full | WLP-VBL: A Robust Lightweight Model for Water Level Prediction |
| title_fullStr | WLP-VBL: A Robust Lightweight Model for Water Level Prediction |
| title_full_unstemmed | WLP-VBL: A Robust Lightweight Model for Water Level Prediction |
| title_short | WLP-VBL: A Robust Lightweight Model for Water Level Prediction |
| title_sort | wlp vbl a robust lightweight model for water level prediction |
| topic | water level variational-mode-decomposition bat algorithm long short-term memory |
| url | https://www.mdpi.com/2079-9292/12/19/4048 |
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