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Article

A Comparison of BPNN, GMDH, and ARIMA for Monthly Rainfall Forecasting Based on Wavelet Packet Decomposition

1
Henan Key Laboratory of Water Resources Conservation and Intensive Utilization in the Yellow River Basin, College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
2
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
3
China Institute of Water Resources and Hydropower Research, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Academic Editor: Momcilo Markus
Water 2021, 13(20), 2871; https://doi.org/10.3390/w13202871
Received: 6 September 2021 / Revised: 7 October 2021 / Accepted: 12 October 2021 / Published: 14 October 2021
(This article belongs to the Special Issue Climate Changes and Hydrological Processes)
Accurate rainfall forecasting in watersheds is of indispensable importance for predicting streamflow and flash floods. This paper investigates the accuracy of several forecasting technologies based on Wavelet Packet Decomposition (WPD) in monthly rainfall forecasting. First, WPD decomposes the observed monthly rainfall data into several subcomponents. Then, three data-based models, namely Back-propagation Neural Network (BPNN) model, group method of data handing (GMDH) model, and autoregressive integrated moving average (ARIMA) model, are utilized to complete the prediction of the decomposed monthly rainfall series, respectively. Finally, the ensemble prediction result of the model is formulated by summing the outputs of all submodules. Meanwhile, these six models are employed for benchmark comparison to study the prediction performance of these conjunction methods, which are BPNN, WPD-BPNN, GMDH, WPD-GMDH, ARIMA, and WPD-ARIMA models. The paper takes monthly data from Luoning and Zuoyu stations in Luoyang city of China as the case study. The performance of these conjunction methods is tested by four quantitative indexes. Results show that WPD can efficiently improve the forecasting accuracy and the proposed WPD-BPNN model can achieve better prediction results. It is concluded that the hybrid forecast model is a very efficient tool to improve the accuracy of mid- and long-term rainfall forecasting. View Full-Text
Keywords: monthly rainfall forecasting; back-propagation neural network; group method of data handing; autoregressive integrated moving average; wavelet packet decomposition monthly rainfall forecasting; back-propagation neural network; group method of data handing; autoregressive integrated moving average; wavelet packet decomposition
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MDPI and ACS Style

Wang, W.; Du, Y.; Chau, K.; Chen, H.; Liu, C.; Ma, Q. A Comparison of BPNN, GMDH, and ARIMA for Monthly Rainfall Forecasting Based on Wavelet Packet Decomposition. Water 2021, 13, 2871. https://doi.org/10.3390/w13202871

AMA Style

Wang W, Du Y, Chau K, Chen H, Liu C, Ma Q. A Comparison of BPNN, GMDH, and ARIMA for Monthly Rainfall Forecasting Based on Wavelet Packet Decomposition. Water. 2021; 13(20):2871. https://doi.org/10.3390/w13202871

Chicago/Turabian Style

Wang, Wenchuan, Yujin Du, Kwokwing Chau, Haitao Chen, Changjun Liu, and Qiang Ma. 2021. "A Comparison of BPNN, GMDH, and ARIMA for Monthly Rainfall Forecasting Based on Wavelet Packet Decomposition" Water 13, no. 20: 2871. https://doi.org/10.3390/w13202871

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