水力发电2013，Vol.39Issue(1)：23-26,66,5.
基于误差修正的BP神经网络含沙量预报模型
Sediment Prediction Model Based on BP Neural Network Theory and Error Correction
摘要
Abstract
Taking the reach from Longmen to Tongguan in Yellow River as study area, a BP neural network model is built to forecast the duration of sediment concentration in Tongguan Hydrological Station after analyzing the impact of sand and water runoff on sediment concentration. At the same time, an error self-regression model is also built based on error sequence to calibrate forecasting results. The durations of sediment concentration before and after calibration are compared, and the results show that the forecasting precision of calibrated duration of sediment concentration is significantly improved and the average uncertainty coefficient of five sediment delivery processes is increase to 0.76 from 0.35.关键词
含沙量/BP神经网络/误差自回归/水文预报/黄河中游Key words
esediment concentration/ BP neural network/ error self-regression/ hydrological forecasting/ middle reaches of Yellow River分类
数理科学引用本文复制引用
黄清烜,梁忠民,曹炎煦,霍世青,许珂艳..基于误差修正的BP神经网络含沙量预报模型[J].水力发电,2013,39(1):23-26,66,5.基金项目
水利部公益性行业科研专项经费基金资助项目(2009101016) （2009101016）
江苏省高校优势学科建设工程基金资助项目 （）
