BP模型在河流水质预测中的误差分析

为验证BP模型在河流水质预测中的有效性,利用仿真技术模拟一条河流污染物的变化趋势,并得到大量的河流水质参数数据.用上游已知河段的水质数据预测该河流下游10个检测断面的水质状况.预测过程分2种情况进行:长距离预测(一次连续预测下游10个河段)和短距离预测(每次连续预测下游2个河段),并以MSE函数生成均方误差作为对2种预测方法性能的检验.结果显示,长距离预测的性能低于短距离预测,2种方法对溶解氧预测的均方误差为0.432和0.035,对生化需氧量预测的均方误差分别为0.243、0.055.

The errors analysis for river water quality prediction based-on BP-modeling

The aim of this study is to describe Error Back Propagation approach that can be used to forecast the water quality. In order to check the validity of the approach, a hypothetical field data as a case study were produced by water quality simulation of a river. ANN was trained by using the upriver data in a river to predict the water quality of lower reaches of the river, which can be predicted by two methods namely long- distance prediction and short-distance prediction. Trying to analyze the validity of two methods using the MSE function in MATLAB 7 indicated the predicted results from short-distance approach were more accurate than that of long-distance prediction. The mean square errors of Dissolved Oxygen were 0. 432 for long- distance prediction and 0. 035 for short-distance prediction while the mean square errors of Biochemical Oxygen Demand were 0. 243 and 0. 055.