Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

Abstract

Land area optimization for horizontal flow constructed wetlands (HFCWs) with a low organic loading rate (OLR) needs special considerations as the microflora changes dramatically with the OLR. The P-k-C* approach does not lead to an accurate calculation of k-values in these wetlands. In this research, nonlinear machine learning models [Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN)] are applied to predict realistic k-values. Data from 37 low-OLR HFCWs (n = 544) were analyzed, and the k-values calculated for these wetlands were found to vary markedly (0.059−0.249 with an average of 0.113 ± 0.090 m/day). The classification of k-values based on the OLR, applied loading rate, and media depth leads to the reduction in standard deviations (SDs) from 83.40 to 35.27%. kvalues with the least SDs are needed for optimal design for low-OLR CWs. The SVR, RF, and ANN models were tested, and the best prediction efficiency on testing datasets was achieved through the ANN model with R 2 (k TKN )= 0.768 (RMSE = 0.067) for total Kjeldahl nitrogen (TKN), R 2 (k TN )= 0.835 (RMSE = 0.043) for total nitrogen (TN), and R 2 (k TP ) = 0.723 (RMSE = 0.087) for total phosphorus (TP). The outcome was validated using primary data from HFCWs, which also confirmed the superiority of the ANNbased model, which can be used for design customization of low-OLR HFCWs.