Groundwater Depth Forecasting Using a Coupled Model

Manfei Zhang, Yimeng Wang, Xiao Wang, Weibo Zhou and Rodica Luca

Discrete Dynamics in Nature and Society, 2021, vol. 2021, 1-11

Abstract: Accurate and reliable prediction of groundwater depth is a critical component in water resources management. In this paper, a new method based on coupling wavelet decomposition method (WA), autoregressive moving average (ARMA) model, and BP neural network (BP) model for groundwater depth forecasting applications was proposed. The relative performance of the proposed coupled model (WA-ARMA-BP) was compared to the regular autoregressive integrated moving average (ARIMA) and BP models for annual average groundwater depth forecasting using leave-one-out cross-validation (LOO-CV). The variables used to develop and validate the models were average groundwater depth data recorded from 1981 to 2010 in Jinghui Canal Irrigation District in the northwest of China. It was found that the WA-ARMA-BP model provided more accurate annual average groundwater depth forecasts compared to the ARIMA and BP models. The results of the study indicate the potential of the WA-ARMA-BP model in forecasting nonstationary time series such as groundwater depth.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:6614195

DOI: 10.1155/2021/6614195

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