Abstract
Accurate runoff forecasting plays an important role in management and utilization of water resources. This paper investigates the accuracy of hybrid long short-term memory neural network and ant lion optimizer model (LSTM–ALO) in prediction of monthly runoff. As the parameters of long short-term memory neural network (LSTM) have influence on the prediction performance, the parameters of the LSTM are calibrated by using ant lion optimizer. Then the selection of suitable input variables of the LSTM–ALO is discussed for monthly runoff forecasting. Finally, we decompose root mean square error into three parts, which can help us better understanding the origin of differences between the observed and predicted runoff. To test the merits of the LSTM–ALO for monthly runoff forecasting, other models are employed to compare with the LSTM–ALO. The scatter-plots and box-plots are adopted for evaluating the performance of all models. In the case study, simulation results with the historical monthly runoff of the Astor River Basin show that the LSTM–ALO model has higher accuracy than that of other models. Therefore, the proposed LSTM–ALO provides an effective method for monthly runoff forecasting.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (Nos. U1765201, 41571514, 51379080), Hubei Provincial Collaborative Innovation Center for New Energy Microgrid in China Three Gorges University, and the Fundamental Research Funds for the Central Universities (No. 2017KFYXJJ204).
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Yuan, X., Chen, C., Lei, X. et al. Monthly runoff forecasting based on LSTM–ALO model. Stoch Environ Res Risk Assess 32, 2199–2212 (2018). https://doi.org/10.1007/s00477-018-1560-y
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DOI: https://doi.org/10.1007/s00477-018-1560-y