Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia

@article{Ansari2018AnalysingTA,
  title={Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia},
  author={Mozafar Ansari and Faridah Othman and Taher J Abunama and Ahmed El-Shafie},
  journal={Environmental Science and Pollution Research},
  year={2018},
  volume={25},
  pages={12139-12149}
}
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… CONTINUE READING

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