Ansari, Mozafar and Othman, Faridah and Abunama, Taher and El-Shafie, Ahmed (2018) Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia. Environmental Science and Pollution Research, 25 (12). pp. 12139-12149. ISSN 0944-1344, DOI https://doi.org/10.1007/s11356-018-1438-z.
Full text not available from this repository.Abstract
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 (R2) 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.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education and University of Malaya for grants FRGS (FP016-2014A) and UMRG (FL001-13SUS) |
| Uncontrolled Keywords: | ARIMA; Influent; Integrated SVM-NAR model; Recurrent neural network; Support vector machine; Time series model |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 20 Sep 2019 03:26 |
| Last Modified: | 20 Sep 2019 03:26 |
| URI: | http://eprints.um.edu.my/id/eprint/22467 |
Actions (login required)
 |
View Item |
