Neural network and cubist algorithms to predict fecal coliform content in treated wastewater by multi‐soil‐layering system for potential reuse
| Autor: | Laila Mandi, Naaila Ouazzani, Abdessamed Hejjaj, Sofyan Sbahi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Environmental Engineering
Artificial neural network 04 agricultural and veterinary sciences Wastewater 010501 environmental sciences Management Monitoring Policy and Law Reuse 01 natural sciences Pollution Fecal coliform Soil Water Quality Linear regression 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Neural Networks Computer Water quality Waste Management and Disposal Effluent Algorithm 0105 earth and related environmental sciences Water Science and Technology Total suspended solids Mathematics |
| Zdroj: | Journal of Environmental Quality. 50:144-157 |
| ISSN: | 1537-2537 0047-2425 |
| DOI: | 10.1002/jeq2.20176 |
| Popis: | This study aims to find the most accurate machine learning algorithms as compared to linear regression for prediction of fecal coliform (FC) concentration in the effluent of a multi-soil-layering (MSL) system and to identify the input variables affecting FC removal from domestic wastewater. The effluent quality of two different designs of the MSL system was evaluated and compared for several parameters for potential reuse in agriculture. The first system consisted of a single-stage MSL (MSL-SS), and the second system consisted of a two-stage MSL (MSL-TS). The concentration of FC in the effluent of the MSL-TS system was estimated by three machine learning algorithms: artificial neural network (ANN), Cubist, and multiple linear regression (MLR). The accuracy of the models was measured by comparing the real and predicted values. Significant (p |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text