【摘要】 针对河流污染治理、水源管理,提出了融合差分自回归滑动平均ARIMA模型和遗传算法优化的小波神经网络相结合的河流水质预测方法。将采集的河流水质参数时间序列数据,分解为线性和非线性序列,线性数据使用ARIMA模型预测,使用最小二乘法完成了ARIMA模型参数估计。对于经过ARIMA模型处理的非线性残差数据、预测值与原始溶解氧序列之间的线性和非线性关系,采用小波神经网络(WNN)获得预测值,并采用遗传算法的选择、交叉、变异等操作优化网络参数,比传统WNN模型预测精度显著提高。ARIMA模型、小波神经网络、遗传算法优化小波神经网络(GAWNN)和未经遗传算法优化的组合模型预测平均绝对误差分别为0.29%、0.39%、0.26%、0.24%,提出的组合模型预测结果平均绝对误差约0.19%且为最小。结果表明,该组合模型优于单个模型和传统组合模型的预测结果。更多还原

【Abstract】 In view of the river pollution control and water management,this study put forward a hybrid model of autoregressive moving average(ARIMA) model and wavelet neural network combined with genetic algorithm,to predict the river water quality.For time series data of water quality parameters,it includes linear and nonlinear sequences.So using the least square method to estimate the ARIMA model parameters,ARIMA model was used to predict linear data.For the nonlinear relationship among the residual error data,prediction result,and original data,using genetic algorithm to optimize wavelet neural network(WNN) parameters,including selection,crossover and mutation operation,WNN was applied to obtain predicted data,which increased the traditional WNN prediction precision significantly.Experimental results show that the mean absolute error of ARIMA model,wavelet neural network,genetic algorithm optimized wavelet neural network(GAWNN),or the hybrid model without genetic algorithm optimized model prediction results are 0.29%,0.39%,0.26% and 0.24% respectively.The mean absolute error of the combined model prediction is about 0.19%,which is the minimum,indicating that the prediction result is better than that of single model and the hybrid model without genetic algorithm optimized.更多还原