Runoff Prediction Based on Hybrid Clustering with WOA Intervals Mapping Model
Publication: Journal of Hydrologic Engineering
Volume 26, Issue 6
Abstract
Accurate forecasting of daily runoff plays an important role in water resource management. This paper presents a feed-forward neural network interval-mapping-model-based clustering analysis technique and the and whale optimization algorithm (C-BPELM-WOAM) for the prediction intervals of the daily runoff series. The proposed model is composed of two parts. One part is the daily runoff point prediction. In this part, a combination of the error unequal-weight coefficient with the error back-propagation training and extreme learning machine algorithms was applied to construct a feed-forward neural network (BPELM), which can improve the performance of the prediction model. The second part is a clustering interval-mapping prediction model based on the whale optimization algorithm (WOA). In this part, -means clustering was used to classify the daily runoff series data into several groups. Then the interval-mapping coefficients corresponding to each group of data were optimized by the WOA so that the prediction interval could be obtained. Finally, the daily runoff data for the Astor River basin were used to verify the efficiency of the C-BPELM-WOAM for daily runoff prediction intervals. The results showed that the C-BPELM-WOAM model obtained higher quality daily runoff prediction intervals.
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Data Availability Statement
The code and data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant No. 52079101, No. U1765201) and the Open Fund of Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station in China Three Gorges University (Grant No. 2019KJX02).
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Journal of Hydrologic Engineering
Volume 26 • Issue 6 • June 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 22, 2020
Accepted: Jan 13, 2021
Published online: Mar 27, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 27, 2021
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