基于人工神经网络的梯形闸门流量计算模型  

Flow Calculation Model for Trapezoidal Gate Based on Artificial Neural Network

作　　者：孟万尚 赵帅杰 李琳[1,2] MENG Wan-shang;ZHAO Shuai-jie;LI Lin(College of Hydraulic and Civil Engineering,Xinjiang Agricultural University,Urumqi 830052,China;Xinjiang Key Laboratory of Hydraulic Engineering Security and Water Disasters Prevention,Urumqi 830052,China)

机构地区：[1]新疆农业大学水利与土木工程学院,乌鲁木齐830052 [2]新疆水利工程安全与水灾害防治重点实验室,乌鲁木齐830052

出　　处：《长江科学院院报》2024年第9期86-92,共7页Journal of Changjiang River Scientific Research Institute

基　　金：新疆维吾尔自治区自然科学基金项目(2022D01A182)。

摘　　要：对新型平板闸门——梯形闸门自由出流和淹没出流的过闸流量进行计算。基于反向传播(BP)神经网络和径向基函数(RBF)神经网络建立了多变量、多组合单输出的流量计算模型,模型输入变量为边坡系数、闸门开度、闸前总水头、水力半径、闸后收缩水深、下游渠道水深,输出变量为实测流量,利用试验实测数据集对模型进行训练和检验,充分验证后发现2种人工神经网络模型的预测效果良好。人工神经网络模型在梯形闸门自由出流和淹没出流的流量计算上适应性好、预测精度高,对灌区各级渠系上设置的梯形闸门过闸流量可以进行精确预测,实现精准控流。This study introduces an approach for calculating the free flow and submerged flow through flat-trapezoidal gate.The flow calculation model was established based on BP(Back Propagation)neural network and RBF(Radial Basis Function)neural network,with multiple variables and combinations as well as single output.The input variables for the model included slope coefficient,gate opening,total head in front of the gate,hydraulic radius,contraction depth behind the gate,and downstream channel water depth.The output variable was measured flow rate.The model was trained and tested using experimental data,and extensive validation confirmed that both BP and RBF artificial neural network models demonstrated strong predictive performance.These models exhibited excellent adaptability and high accuracy in predicting flow rates for trapezoidal gates in canal systems in irrigation areas,thereby enabling precise flow control.

关 键 词：梯形闸门 自由出流 淹没出流 BP神经网络 RBF神经网络 流量预测 

分 类 号：TV131[水利工程—水力学及河流动力学]