基于BP神经网络的松花江四方台水质预测

为实现松花江四方台CODMn的预测,应用人工神经网络技术(ANN),在预测模型中采用LM(Levenberg-Marquardt)算法提高网络的收敛速度,并采用提前停止法提高网络的推广能力.ANN样本集数据来源于1997～1999年3年的四方台监测站日检测水质数据.采用拉依达准则法剔除样本集异常数据.为更有效地评估预测模型的准确性,将松花江分为丰雨期、封冻期和其他月份来分别考察预测效果.并对1999年以后近期与远期的水质预测效果进行对比.结果表明:丰雨期预测效果最差,封冻期最好,其他月份介于之间,模型对近期水质的预测效果要明显好于对远期的预测效果.整体预测效果不错,可用于指导实际的水质管理.

Water quality forecast through application of BP neural network at Sifangtai

To forecast the CODMn at Sifangtai of Songhua River,ANN technique was employed to establish a forecast model. It adopted LM (Levenberg-Marquardt) algorithm to achieve a higher convergent speed,and the early stop method to improve the extended capacity. Sample data of ANN were selected from daily measured values of Sifangtai Station from 1997 to 1999,and the Layida rule was adopted to eliminate abnormal data. To evaluate the veracity of this model,forecast effects of the abundant rain period,freezing period and other periods of Songhua River were investigated. And the forecast effects in recent and long terms after 1999 were compared. It is indicated that the forecast effect in abundant rain period is the worst,that in freezing period is the best and that in other periods is in the middle;the forecast effect in recent term is better than that in long term. The whole forecasting effects are good,and the model can be used for water quality management of Songhua River.