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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Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 19; p. 4048
Main Authors Yi, Congqin, Huang, Wenshu, Pan, Haiyan, Dong, Jinghan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2023
Subjects
Accuracy
Algorithms
Back propagation
Datasets
Decomposition
Hydroelectric power
Hydrology
Lightweight
Machine learning
Neural networks
Prediction models
Predictions
Regression analysis
Remote sensing
Reservoirs
Resource scheduling
Robustness
Sea level
Support vector machines
Time series
Trends
Water levels
Water resources management
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12194048