Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models

Zhao, Xuehua and Lv, Hanfang and Wei, Yizhao and Lv, Shujin and Zhu, Xueping (2021) Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models. Water, 13 (1). p. 91. ISSN 2073-4441

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Abstract

Data-intelligent methods designed for forecasting the streamflow of the Fenhe River are crucial for enhancing water resource management. Herein, the gated recurrent unit (GRU) is coupled with the optimization algorithm improved grey wolf optimizer (IGWO) to design a hybrid model (IGWO-GRU) to carry out streamflow forecasting. Two types of predictive structure-based models (sequential IGWO-GRU and monthly IGWO-GRU) are compared with other models, such as the single least-squares support vector machine (LSSVM) and single extreme learning machine (ELM) models. These models incorporate the historical streamflow series as inputs of the model to forecast the future streamflow with data from January 1956 to December 2016 at the Shangjingyou station and from January 1958 to December 2016 at the Fenhe reservoir station. The IGWO-GRU model exhibited a strong ability for mapping in streamflow series when the parameters were carefully tuned. The monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The monthly IGWO-GRU model was found to be a better forecasting tool, with an average qualification rate of 91.66% in two stations. It also showed good performance in absolute error and peak flow forecasting.

Item Type: Article
Uncontrolled Keywords: gated recurrent unit; improved grey wolf optimizer; monthly streamflow forecasting; data-driven modeling
Subjects: STM Repository > Biological Science
Depositing User: Managing Editor
Date Deposited: 31 Jul 2024 12:29
Last Modified: 31 Jul 2024 12:29
URI: http://classical.goforpromo.com/id/eprint/806

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