New approach to predict fecal coliform removal for storm water biofilters application. IIUM Engineering Journal, 23 (2). pp. 58-45. ISSN 1511-788X (2022)
Abstract
Fecal coliform removal using storm water biofilters is an important aspect of storm water management. A model that can provide an accurate prediction of fecal coliform removal is essential. Therefore, feedforward backpropagation neural network (FBNN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed using a range of input features, namely grass type, the thickness of biofilter, and initial concentration of E. coli, while the estimated final concentration of E. coli was the output variable. The ANFIS model shows a better overall performance than the FBNN model, as it has a higher R2-value of 0.9874, lower MAE and RMSE values of 3.854 and 6.004 respectively, and a smaller average percentage error of 14.2%. Hence, the proposed ANFIS model can be served as an advanced alternative to replace the need for laboratory work.
| Item Type: | Article |
|---|---|
| Keywords: | Artificial intelligence, Biofilters, Fecal coliform, Neural network, Storm water |
| Taxonomy: | By Subject > College of Engineering > Civil Engineering > Water Resources and Environment |
| Local Content Hub: | Subjects > College of Engineering |
| Depositing User: | Eza Eliana Abdul Wahid |
| Date Deposited: | 07 Dec 2022 04:56 |
| Last Modified: | 07 Dec 2022 04:56 |
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