朱 咏1，陈学林2

(1.甘肃省张掖水文水资源勘测局，甘肃 张掖 734000;2.甘肃省水文水资源局，甘肃 兰州 730000)

　　摘 要：分析天然河道糙率值的主要影响因素，基于LM-BP神经网络预测模型，提出黑河莺落峡水文站测验河段糙率值的推求方法。对网络模型的设计、训练和测试进行研究和分析，预测模型输入量为水位Z、水力半径R及水面比降S，输出量为糙率n，样本集为黑河莺落峡水文站测验河段的历史实测糙率值。根据测试结果可以得出，在误差允许范围内，采用LM-BP神经网络预测模型可较准确地推求不同水位、比降对应的n值,该糙率值可应用到河道高洪水期比降面积测流中。

　　关键词：糙率;LM-BP神经网络;天然河道;推测

　　The speculating research of friction in the Yingluoxia channel based on LM-BP neural network

　　ZHU Yong1, CHEN Xuelin2

　　(1.Zhangye Hydrology and Water Resources Bureau of Gansu Province, Zhangye 734000, China;

　　2. Hydrology and Water Resources Bureau of Gansu Province, Lanzhou 73000,China)

　　Abstract: The article analyzes the main influencing factors in the process of natural channel roughness value. Based on the LM-BP neural network prediction model, it proposes a method to calculate the roughness value of the tested reach of the Yingluoxia hydrological station in Heihe River, researches and analyzes the design, training and testing of network model. The input of the prediction model is water level z, hydraulic radius R and water surface slope S; the output is roughness n. The sample set is the historical measured roughness value of the tested reach of Yingluoxia Hydrometric Station in Heihe River. According to the test results, within the allowable error range, the n value corresponding to different water level and specific water surface slope can be accurately calculated by LM-BP neural network prediction model. The n value can be applied to the flow measurement of slope-area in high flood period.

　　Key words: roughness; LM-BP neural network; natural channel; speculation