| Research on monthly runoff prediction of VMD-LSTM model in different forecast periods |
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| DOI：10.7606/j.issn.1000-7601.2022.06.28 |
| Key Words: monthly runoff prediction variable modal decomposition long short\|term memory neural network atmospheric circulation forecast period |
| Author Name | Affiliation | | QI Jixia | College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi 712100，China | | SU Xiaoling | College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi 712100，China
Key Laboratory of Arid Area Agricultural Water and Soil Engineering, Ministry of Education, Northwest A＆F University, Yangling, Shaanxi 712100, China | | ZHANG Gengxi | Key Laboratory of Arid Area Agricultural Water and Soil Engineering, Ministry of Education, Northwest A＆F University, Yangling, Shaanxi 712100, China | | ZHANG Te | Key Laboratory of Arid Area Agricultural Water and Soil Engineering, Ministry of Education, Northwest A＆F University, Yangling, Shaanxi 712100, China |
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| Abstract: |
| In this study, Variational Mode Decomposition (VMD) and Long Short\|Term Memory (LSTM) neural network were integrated to establish a hybrid prediction model. The named VMD-LSTM model was to reduce the prediction error caused by the nonlinearity and non\|stationarity of the runoff series and improve the accuracy of monthly runoff prediction results under various forecast periods. Some highly correlated atmospheric circulation factors were selected as the additional term of the model input to predict the monthly runoff for 1~ 3 lead months. The performance of VMD-LSTM in predicting monthly runoff at the Tangnaihai, Minhe, Xiangtang, Hongqi and Zheqiao stations at the upper reaches of the Yellow River Basin was verified. The VMD-LSTM model was compared with VMD-BP (BP neural network), VMD-SVR (support vector regression) and the single LSTM model for evaluating its applicability.The results showed that the VMD-LSTM model exhibited the best forecasting performance, compared with the single LSTM model, and its Nash efficiency coefficient (NSE) was substantially improved from 0.6~0.7 to above 0.9. When putting atmospheric circulation factors, the accuracy of VMD-LSTM model was further improved, with NSE remaining at 0.91~0.96. With the increase of lead time, the precision attenuation of VMD-LSTM model became slower than VMD-BP and VMD-SVR model, and its NSE still remained at 0.84~0.95 when the forecast period was 3 months. The VMD-LSTM model is an effective method for monthly runoff prediction, and the results can provide guidance for monthly runoff prediction in the study area. |
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