结合灰色理论的人工神经网络方法 在水质预测中的应用

针对现阶段水质监测中存在的水质变化响应滞后问题,提出了采用灰色预测法、人工神经网络(BP神经网络、径向基神经网络、广义回归神经网络)以及两者组合的方法对水质动态预测进行研究。以太湖流域嘉兴斜路港监测断面为例,并依据后验差检验比值c及小概率精度p对模型预测效果进行了分析。结果表明,对年内预测,通过广义回归神经网络的动态预测值平均相对误差为0.61%,后验差检验比值小于0.65,小误差概率大于0.7;采用灰色结合广义回归神经网络的方法对水质pH值进行预测,平均相对误差仅有0.85%,后验差检验比值小于0.65,小误差概率等于1。研究结果还表明,对年际预测,灰色结合BP神经网络和灰色结合径向基函数神经网络的动态预测值平均相对误差分别为0.57%和0.80%,其后验差比值都小于0.5,小概率误差都为0.9,大于0.8。

Study on the prediction of water quality based on artificial neural network combined with grey theory

In this paper, the gr ey theor y, a rtificial neural netw or k ( back2 pr opagation neura l netwo rk, radial basis function neural netw or k, and g ener alized regr ession neural net wo rk) , and the combinat ion of these two metho ds w as pr oposed to st udy the dy2 namic predictio n o f water quality. Taking the Xielug ang in Jiax in as an example, the model prediction effect w as analy zed based on the posterior difference test r atio ( c) and small pro bability accuracy ( p ) . T he r esults show ed t hat within a predict ion y ear, the av erag e relat ive err or of the dynamic predictio n value of the g ener alized r egr essio n neura l netw ork was 01 61% , and the c was less than 01 65, w hile the p w as g reater than 01 7, respectively . The r esults ex hibited that the predictio n value using the combinat ion of the gr ey theo ry and g ener alized r eg ressio n neur al netw or k, the av erag ed relative err or w as 01 85% , the c< 01 65, and the p= 11 0, r espectiv ely. The inter2y ea r pr edict ion based on the combinatio n o f the gr ey theor y with BP neura l netw or k and radial basis function neura l netw or k, the av eraged relat ive err or w as 01 57% and 01 80, respectively , and the ratio o f posterior er2 r or was less than 01 5, and the small pro bability err or w as 01 9, but g reater t han 01 8.