Abstract
The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan’an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.
Highlights
Precipitation is the main source of recharge for water resources, and it is a key component of the water cycle
Zhang et al [34] proposed a new hybrid model, ensemble empirical mode decomposition (EEMD) with Elman neural network, for annual runoff time series forecasting in the Dongting Lack basin, and the results showed that this proposed model gave a good performance
A hybrid forecasting model based on variational mode decomposition (VMD) and extreme learning machine (ELM) is proposed to improve the prediction accuracy of monthly precipitation time series
Summary
Precipitation is the main source of recharge for water resources, and it is a key component of the water cycle. BP neural network has been used to predict monthly precipitation and has higher accuracy than traditional time series models [13]. Zhang et al [34] proposed a new hybrid model, EEMD with Elman neural network, for annual runoff time series forecasting in the Dongting Lack basin, and the results showed that this proposed model gave a good performance. Lahmiri [25] proposed a hybrid model combining VMD and BP neural network for intraday stock price forecasting, and the results showed that this proposed model gave a good performance. In this paper, a new hybrid model based on VMD coupled with ELM neural network is proposed for monthly precipitation time series forecasting. This paper is organized as follows: Section 2 briefly describes the basic theory of VMD, ELM, and the hybrid VMD-ELM; Section 3 provides the case study analysis, which introduces the research data and performs decomposition preprocessing, the evaluation index of the prediction accuracy, and the analysis of the prediction results of each model; Section 4 presents the conclusion of the paper
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