Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
Environ Sci Pollut Res Int. 2018 Apr;25(12):12139-12149. doi: 10.1007/s11356-018-1438-z. Epub 2018 Feb 17.
The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models. An integrated model was developed based on the individual models' prediction ability for low, average and peak flow. An initial assessment of the results showed that the ARIMA model was the least accurate and the NAR model was the most accurate. The RE results also prove that the SVM model's frequency of errors above 10% or below - 10% was greater than the NAR model's. The influent was also forecasted up to 44 weeks ahead by both models. The graphical results indicate that the NAR model made better predictions than the SVM model. The final evaluation of NAR and SVM demonstrated that SVM made better predictions at peak flow and NAR fit well for low and average inflow ranges. The integrated model developed includes the NAR model for low and average influent and the SVM model for peak inflow.
污水处理厂的功能是将污水处理到可接受的标准，然后再排放到接收水中。为了设计和运行这样的工厂，有必要测量和预测进水流量。在这项研究中，采用自回归综合移动平均（ARIMA）、非线性自回归网络（NAR）和支持向量机（SVM）回归时间序列算法对污水处理厂（STP）的进水流量进行建模和预测。为了评估模型的准确性，计算了均方根误差（RMSE）和确定系数（R）作为初始评估指标，而相对误差（RE）、峰值流量标准（PFC）和低流量标准（LFC）作为最终评估指标，以展示所选模型的详细准确性。基于各个模型的低、中、高流量预测能力，开发了一个综合模型。结果的初步评估表明，ARIMA 模型的准确性最低，NAR 模型的准确性最高。RE 结果也证明了 SVM 模型的误差频率高于 10%或低于-10%的次数大于 NAR 模型的次数。这两种模型都可以对未来 44 周的进水进行预测。图形结果表明，NAR 模型的预测结果优于 SVM 模型。对 NAR 和 SVM 的最终评估表明，SVM 模型在高峰流量时的预测效果更好，而 NAR 模型在低流量和平均流量范围内拟合得更好。开发的综合模型包括 NAR 模型用于低流量和平均流量，SVM 模型用于高峰流量。
