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