Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Regional characteristics’ impact on the performances of the gated recurrent unit on streamflow forecasting
The gated recurrent unit (GRU) has obtained attention as a potential model for streamflow forecasting in recent years. Common patterns and specialties when employing it in different regions, as well as a comparison between different models still need investigation. Therefore, we examined the performances of GRU for one, two, and three-day-ahead streamflow forecasting in seven basins in various geographic regions in China from the aspect of robustness, overall accuracy, and accuracy of streamflow peaks’ forecasting. The robustness and accuracy of it are closely related to correlations between the input and forecasting target series. Also, it outperforms the benchmark machine learning models in more cases, especially for one-day-ahead forecasting (NSE of 0.88–0.96 except for the unsatisfactory result in the Luanhe River basin). The deterioration of its accuracy along the increasing lead time depends on the dominant time lags between the rainfall and streamflow peaks. Recommendations were proposed for further applications. HIGHLIGHTS An evaluation of GRU versus benchmark models for streamflow forecasting in diverse regions.; The problem that how do data and basins' characteristics affect the model's performance was discussed.; The summarized patterns are valuable for a quick applicability evaluation and data selection process in further applications.;
Regional characteristics’ impact on the performances of the gated recurrent unit on streamflow forecasting
The gated recurrent unit (GRU) has obtained attention as a potential model for streamflow forecasting in recent years. Common patterns and specialties when employing it in different regions, as well as a comparison between different models still need investigation. Therefore, we examined the performances of GRU for one, two, and three-day-ahead streamflow forecasting in seven basins in various geographic regions in China from the aspect of robustness, overall accuracy, and accuracy of streamflow peaks’ forecasting. The robustness and accuracy of it are closely related to correlations between the input and forecasting target series. Also, it outperforms the benchmark machine learning models in more cases, especially for one-day-ahead forecasting (NSE of 0.88–0.96 except for the unsatisfactory result in the Luanhe River basin). The deterioration of its accuracy along the increasing lead time depends on the dominant time lags between the rainfall and streamflow peaks. Recommendations were proposed for further applications. HIGHLIGHTS An evaluation of GRU versus benchmark models for streamflow forecasting in diverse regions.; The problem that how do data and basins' characteristics affect the model's performance was discussed.; The summarized patterns are valuable for a quick applicability evaluation and data selection process in further applications.;
Regional characteristics’ impact on the performances of the gated recurrent unit on streamflow forecasting
Qianyang Wang (Autor:in) / Yuexin Zheng (Autor:in) / Qimeng Yue (Autor:in) / Yuan Liu (Autor:in) / Jingshan Yu (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting
| DOAJ | 2025
An explainable Bayesian gated recurrent unit model for multi-step streamflow forecasting
| Elsevier | 2025
Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models
| DOAJ | 2021
Predicting day-ahead solar irradiance through gated recurrent unit using weather forecasting data
| American Institute of Physics | 2019
Combining STRIPAT model and gated recurrent unit for forecasting nature gas consumption of China
| Online Contents | 2020