Improving annual precipitation prediction using extreme learning machine with wavelet packet decomposition

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action as well as activity. In this paper, a novel merged precipitation prediction framework (ELM-WPD) is proposed on the Extreme learning machine (ELM) with wavelet packet decomposition (WPD). The model can be described as the following: (a) we use the WPD to decompose the original precipitation data into several sub-layers; (b) ELM model is employed to complete the forecasting calculation for the decomposed series; (c) the results are integrated to complete the final prediction. Four quantitative indexes (RMSE, MAE, R and NSEC) are employed for the comparison criteria. The results are compared with Back-propagation neural network (BPNN), autoregressive integrated moving average model (ARIMA), ELM, BPNN-WPD model, ARIMA-WPD, indicating that the ELM-WPD model has better performance than other models used in this paper. Hence, the proposed method can provide higher accuracy and reliability for annual precipitation forecasting and can be extended to similar situations for precipitation forecasting.