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A platform for research: civil engineering, architecture and urbanism
Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands
https://orcid.org/0000-0001-9790-2518
https://orcid.org/0000-0002-1766-9646
https://orcid.org/0000-0002-1911-0862
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%. k-values 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 ANN-based model, which can be used for design customization of low-OLR HFCWs.
This paper presents the optimum design process of organic-deficient horizontal flow constructed wetlands employing machine learning algorithms for achieving high removal efficacy for nutrients.
Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands
https://orcid.org/0000-0001-9790-2518
https://orcid.org/0000-0002-1766-9646
https://orcid.org/0000-0002-1911-0862
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%. k-values 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 ANN-based model, which can be used for design customization of low-OLR HFCWs.
This paper presents the optimum design process of organic-deficient horizontal flow constructed wetlands employing machine learning algorithms for achieving high removal efficacy for nutrients.
Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands
https://orcid.org/0000-0001-9790-2518
https://orcid.org/0000-0002-1766-9646
https://orcid.org/0000-0002-1911-0862
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%. k-values 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 ANN-based model, which can be used for design customization of low-OLR HFCWs.
This paper presents the optimum design process of organic-deficient horizontal flow constructed wetlands employing machine learning algorithms for achieving high removal efficacy for nutrients.
Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands
Singh, Saurabh (author) / Soti, Abhishek (author) / Kulshreshtha, Niha Mohan (author) / Samaria, Akshat (author) / Brighu, Urmila (author) / Gupta, Akhilendra Bhushan (author) / Bezbaruah, Achintya N. (author)
ACS ES&T Water ; 4 ; 2619-2631
2024-06-14
Article (Journal)
Electronic Resource
English
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