Author(s): Wei Cui; Zhinan Ding; Xiangpeng Mu; Wenxue Chen; Yuling Lei; Zihou Niu; Hui Liu

Linked Author(s):

Keywords: South-to-North Water Diversion Project; Control gate; BP neural network; One-dimensional unsteady gradually varied flow; Water level rise prediction

Abstract: When accidents occur in open-channel water conveyance projects, it is often necessary to rapidly close control gates. Accurately estimating the upstream water level rise is crucial for developing a reasonable gate closure plan. This study focuses on a specific section of the Middle Route of the South-to-North Water Diversion Project as the study object, constructing a water level estimation model based on the BP neural network. Due to the lack of rapid gate closure sample data from the project, a one-dimensional unsteady gradually varied flow simulation model is developed. After calibration with measured data, the model was used to train the neural network. Through simulation analysis, the primary factors influencing the upstream water level rise were identified: gate closure amplitude, gate closure duration, and operational water level, which serve as the input variables for the neural network model. Based on tests from 25 cases, the BP neural network model demonstrated a maximum positive deviation of 0.034 m and a maximum negative deviation of -0.020 m in water level estimation, achieving an accuracy sufficient for engineering applications.

DOI: https://doi.org/10.64697/978-90-835589-7-4_41WC-P1552-cd

Year: 2025