Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters.
Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters.
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DOI：
10.1186/2052-336x-12-40
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发表时间：
2014-01-23
影响因子：
3.4
通讯作者：
Zare Abyaneh H
中科院分区：
文献类型：
--
作者：
Zare Abyaneh H
本文研究了多元线性回归（MLR）和人工神经网络（ANN）模型在污水处理厂两个主要水质参数预测中的有效性。生化需氧量（BOD）和化学需氧量（COD）以及有机物的间接指标是下水道水质的代表性参数。使用相关系数（r），均方根误差（RMSE）和偏差值的ANN模型的性能进行了评价。BOD和COD的模型计算值、人工神经网络方法计算值和回归分析值与实测值吻合较好。结果表明，人工神经网络性能模型优于MLR模型。以温度（T）、pH、总悬浮物（TSS）和总悬浮物（TS）为输入值的优化神经网络预测BOD的比较指标为RMSE = 25.1mg/L，r = 0.83，预测COD的比较指标为RMSE = 49.4mg/L，r = 0.81。结果表明，人工神经网络模型可以成功地应用于污水生化处理厂进水BOD和COD的预测。敏感性试验结果表明，pH参数对BOD和COD的预测影响较大。此外，这两个实现的模型预测BOD比COD更好。
This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD.