Monthly runoff forecasting based on LSTM–ALO model

• Published in: Stochastic environmental research and risk assessment Vol. 32; no. 8; pp. 2199 - 2212
• Main Authors: Yuan, Xiaohui, Chen, Chen, Lei, Xiaohui, Yuan, Yanbin, Muhammad Adnan, Rana
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2018; Springer Nature B.V
• Subjects: Aquatic Pollution; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Computer simulation; Earth and Environmental Science; Earth Sciences; Environment; Forecasting; Long short-term memory; Math. Appl. in Environmental Science; Mathematical models; Model accuracy; Neural networks; Original Paper; Parameters; Physics; Predictions; Probability Theory and Stochastic Processes; Resource management; River basins; Rivers; Runoff; Runoff forecasting; Statistics for Engineering; Waste Water Technology; Water Management; Water Pollution Control; Water resources; Monthly runoff forecasting; Long short-term memory neural network; Errors decomposition; Ant lion optimizer
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-018-1560-y

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.