A Hybrid Model of Ensemble Empirical Mode Decomposition and Sparrow Search Algorithm-Based Long Short-Term Memory Neural Networks for Monthly Runoff Forecasting

Abstract

Monthly runoff forecasting plays a vital role in reservoir ecological operation, which can reduce the negative impact of dam construction and operation on the river ecosystem. Numerous studies have been conducted to improve monthly runoff forecast accuracy, of which machine learning methods have been paid much attention due to their unique advantages. In this study, a conjunction model, EEMD-SSA-LSTM for short, which comprises ensemble empirical mode decomposition (EEMD) and sparrow search algorithm (SSA)–based long short-term neural networks (LSTM), has been proposed to improve monthly runoff forecasting. The EEMD-SSA-LSTM model is mainly carried out in three steps. First, the original time series data is decomposed into several sub-sequences. Second, each sub-sequence is simulated by LSTM, of which the hyperparameters are optimized by SSA. Finally, the simulated results for each sub-sequence are summarized as the final results. The data obtained from two reservoirs located in China are used to validate the proposed model performance. Meanwhile, four commonly used statistical evaluation indexes are utilized to evaluate model performance. The results demonstrate that compared to several benchmark models, the proposed model can yield satisfactory forecast results and can be conducive to improving monthly runoff forecast accuracy.