CAJ | 학술논문

水质变化具有非线性、突变性，且含有噪声，传统线性预测模型不能全面反映其变化规律，预测精度低，误差大。针对水质变化规律复杂，影响因素间非线性程度高的问题，为了提高水质预测精度，将改进算法的BP神经网络引入化学需氧量( COD)预测预报领域，以pH、溶解氧( DO)、氨氮( NH3-N)为输入向量，以COD为输出向量，建立了COD的预测模型并对效果进行检验。结果表明：检验样本中COD的预测值与实测值的线性相关系数为0.991。 BP神经网络模型预测精度高，收敛速度快，具有良好的泛化能力，能较好地反映COD和影响因子的变化规律。
수질변화구유비선성、돌변성，차함유조성，전통선성예측모형불능전면반영기변화규률，예측정도저，오차대。침대수질변화규률복잡，영향인소간비선성정도고적문제，위료제고수질예측정도，장개진산법적BP신경망락인입화학수양량( COD)예측예보영역，이pH、용해양( DO)、안담( NH3-N)위수입향량，이COD위수출향량，건립료COD적예측모형병대효과진행검험。결과표명：검험양본중COD적예측치여실측치적선성상관계수위0.991。 BP신경망락모형예측정도고，수렴속도쾌，구유량호적범화능력，능교호지반영COD화영향인자적변화규률。
Water quality change is of nonlinear and dynamicity,it is a kind of complex time series data,therefore,the traditional linear pre-diction model cannot reflect the variation rule,and the prediction accuracy is low. For the problems of complex water quality change rule and high degree of nonlinear between factors,in order to improve the water quality prediction accuracy,introduce the BP neural network of improved algorithm into a model of COD,with pH,DO,NH3-N as input and COD as output,the prediction model of COD is estab-lished and tested. The research results show the linear correlation coefficient of COD between forecasting and the monitoring in the test samples is 0. 991. BP neural network has high forecast precision,fast convergence rate and the good generalization ability,which can bet-ter reflect the change rule between COD and impact factors.