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Article

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

College of Water Sciences, Beijing Normal University, Beijing 100875, China
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Author to whom correspondence should be addressed.
Water 2020, 12(12), 3532; https://doi.org/10.3390/w12123532
Received: 21 November 2020 / Revised: 15 December 2020 / Accepted: 15 December 2020 / Published: 16 December 2020
(This article belongs to the Special Issue Advanced Hydrologic Modeling in Watershed-Scale)
A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model. View Full-Text
Keywords: ANN; GRU; runoff forecasting; input filtering; architecture ANN; GRU; runoff forecasting; input filtering; architecture
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MDPI and ACS Style

Wang, Q.; Liu, Y.; Yue, Q.; Zheng, Y.; Yao, X.; Yu, J. Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China. Water 2020, 12, 3532. https://doi.org/10.3390/w12123532

AMA Style

Wang Q, Liu Y, Yue Q, Zheng Y, Yao X, Yu J. Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China. Water. 2020; 12(12):3532. https://doi.org/10.3390/w12123532

Chicago/Turabian Style

Wang, Qianyang; Liu, Yuan; Yue, Qimeng; Zheng, Yuexin; Yao, Xiaolei; Yu, Jingshan. 2020. "Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China" Water 12, no. 12: 3532. https://doi.org/10.3390/w12123532

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