Details

Autor(en) / Beteiligte

• Nguyen, Xuan Cuong
• Ly, Quang Viet
• Li, Jianxin
• Bae, Hyokwan
• Bui, Xuan-Thanh
• Nguyen, Thi Thanh Huyen
• Tran, Quoc Ba
• Vo, Thi-Dieu-Hien
• Nghiem, Long D.

Titel

Nitrogen removal in subsurface constructed wetland: Assessment of the influence and prediction by data mining and machine learning

Ist Teil von

• Environmental technology & innovation, 2021-08, Vol.23, p.101712, Article 101712

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2021

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Subsurface constructed wetland (SCW) appears to be an economical and environmental-friendly practice to treat nitrogen-enriched (waste) water. Nevertheless, the removal mechanisms in SCW are complicated and rather time-consuming to conduct and assessment the efficiency of new experiments. This work mined data from literature and developed the machine learning models to elucidate the effect of influent inputs and predict ammonium removal rate (ARR) in SCW treatment. 755 sets and 11 attributes were applied in four modeled algorithms, including Random forest, Cubist, Support vector machines, and K-nearest neighbors. Six out of ten input features including ammonium (NH4), total nitrogen (TN), hydraulic loading rate (HLR), the filter height (i.e., Height), aeration mode (i.e., Aeration), and types of inlet feeding (i.e., Feeding) have posed pronounced influences on the ARR. The Cubist algorithm appears the most optimal model showing the lowest RMSE i.e., 0.974 and the highest R2 i.e., 0.957. The contribution of variables followed the order of NH4, HLR, TN, Aeration, Height and Feeding corresponding to 97, 93, 71, 49, 34, and 34%, respectively. The generalization ability to forecast ARR using testing data achieved the R2 of 0.970 and the RMSE of 1.140 g/m2 d, indicating that Cubist is a reliable tool for ARR prediction. User interface and web tool of final predictive model are provided to facilitate the application for designing and developing SCW system in real practice. •Four supervised ML algorithms were utilized to predict ARR by SCW.•Six over ten input variables were found to be the most pronounced parameters.•NH4-N (97%), HLR (93%), and TN (71%) contributed the most to model.•Cubist achieved the highest generalization with R2 of 0.957 and RMSE of 0.974 g/m2 d.•Strong correlation between the predicted and actual results by Cubist.