Department of Irrigation and Drainage Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.
J Environ Health Sci Eng. 2014 Jan 23;12(1):40. doi: 10.1186/2052-336X-12-40.
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.
本文探讨了多元线性回归（MLR）和人工神经网络（ANN）模型在预测污水处理厂两种主要水质参数中的效率。生化需氧量（BOD）和化学需氧量（COD）以及有机物的间接指标是污水水质的代表性参数。使用相关系数（r）、均方根误差（RMSE）和偏差值来评估 ANN 模型的性能。模型、ANN 方法和回归分析计算的 BOD 和 COD 值与各自的实测值非常吻合。结果表明，ANN 性能模型优于 MLR 模型。对于预测 BOD 的优化 ANN 与输入值温度（T）、pH 值、总悬浮固体（TSS）和总悬浮固体（TS）的比较指标为 RMSE=25.1mg/L，r=0.83，对于预测 COD 的比较指标为 RMSE=49.4mg/L，r=0.81。结果表明，ANN 模型可成功用于估计废水生化处理厂进水的 BOD 和 COD。此外，敏感检查结果表明，pH 值参数对 BOD 和 COD 的预测比对其他参数的预测影响更大。此外，两种实施的模型都更好地预测了 BOD 而不是 COD。
