基于小波神经网络的芦苇潜流人工湿地水质预测

人工湿地系统对污水的处理效果好,工艺简单,投资运行费用低,但影响其出水水质的因素很多,并且往往是非线性的,因此目前很难将这些影响因素模型化并用于水质预测. 已有的预测方法不是过于复杂就是预测精度不高. 神经网络是一种具有较强预测能力的新方法,适用于各种非线性模型的预测. 在小试研究的基础上,使用3种不同的、经过训练的小波神经网络,对芦苇潜流人工湿地沿程各采样口的水温,ρ(DO),pH,E_h和ρ(COD_(Cr))等水质指标进行了预测. 结果显示,各指标的平均相对误差分别为:水温≤4.21%,pH≤1.36%,ρ(DO)≤9.77%,E_h≤6.50%,ρ(COD_(Cr))≤17.76%,表明小波神经网络模型适用于人工湿地模型的预测.

Water Quality Prediction Using Wavelet Neural Networks in Phragmites australis Subsurface Flow Constructed Wetlands

Constructed wetland systems are effective in treating sewage. They have simple process and low investment/operation costs. However, there are many factors influencing the water quality, and these factors are often nonlinear. Therefore, it is difficult to model these factors so as to predict water quality. Some prediction methods are too complicated, while others have a relatively low prediction accuracy. The artificial neural network is an efficient, new method in predicting a variety of non-linear models. On the basis of laboratory experiments, three kinds of trained wavelet neural networks were used to predict water quality along the Phragmites australis subsurface flow constructed wetlands, including water temperature, ρ(DO), pH, E_h and ρ(COD_(Cr)). The prediction results showed that the average relative error of the water temperature≤4.21%, pH≤1.36%, ρ(DO)≤9.77%, E_h≤6.50%, ρ(COD_(Cr))≤17.76%. The results indicated that the wavelet neural networks model can effectively predict various items in water quality of constructed wetlands.