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Improving forecasting accuracy of river flow using gene expression programming based on wavelet decomposition and de-noising

机译：基于小波分解和去噪的基因表达程序提高河流流量预报精度

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摘要

Due to the effects of anthropogenic activities and natural climate change, streamflows of rivers have gradually decreased. In order to maintain reliable water supplies, reservoir operation and water resource management, accurate streamflow forecasts are very important. Based on monthly flow data from five hydrological stations in the middle and lower parts of the Hanjiang River Basin, between 1989 and 2009, we consider an efficient approach of adopting the gene expression programming model based on wavelet decomposition and de-noising (WDDGEP) to forecast river flow. Original flow time series data are initially decomposed into one sub-signal approximation and seven sub-signal details using the dmey wavelet. A wavelet threshold de-noising method is also applied in this study. Data that have been de-noised after decomposition are then adopted as inputs for WDDGEP models. Finally, the forecasted sub-signal results are summed to formulate an ensemble forecast for the original monthly flow series. A comparison of the prediction accuracy between the two models is based on three performance evaluation measures. Results show that the new WDDGEP models can effectively enhance accuracy in forecasting streamflow, and the proposed wavelet-based de-noising of the observed non-stationary time series is an effective measure to improve simulation accuracy.
机译：由于人为活动和自然气候变化的影响，河流的流量逐渐减少。为了维持可靠的水供应，水库运行和水资源管理，准确的流量预测非常重要。基于1989年至2009年之间汉江中下游五个水文站的月流量数据，我们考虑采用一种基于小波分解和降噪（WDDGEP）的基因表达编程模型的有效方法预测河流流量。最初的流动时间序列数据首先使用dmey小波分解为一个子信号近似值和七个子信号细节。小波阈值去噪方法也被应用在这项研究中。然后将分解后已去噪的数据用作WDDGEP模型的输入。最后，将预测的子信号结果相加，以为原始的每月流量序列制定整体预测。两种模型之间的预测准确性比较基于三种性能评估方法。结果表明，新的WDDGEP模型可以有效地提高预测流量的准确性，并且所提出的基于小波的观测到的非平稳时间序列降噪是提高模拟精度的有效措施。

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