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Article

Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds

The Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA
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Water 2021, 13(2), 147; https://doi.org/10.3390/w13020147
Received: 11 November 2020 / Revised: 8 January 2021 / Accepted: 8 January 2021 / Published: 10 January 2021
(This article belongs to the Section Urban Water Management)
Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R2) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R2 values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable. View Full-Text
Keywords: stormwater quality; urban watersheds; machine learning algorithms; total suspended solids stormwater quality; urban watersheds; machine learning algorithms; total suspended solids
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MDPI and ACS Style

Moeini, M.; Shojaeizadeh, A.; Geza, M. Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds. Water 2021, 13, 147. https://doi.org/10.3390/w13020147

AMA Style

Moeini M, Shojaeizadeh A, Geza M. Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds. Water. 2021; 13(2):147. https://doi.org/10.3390/w13020147

Chicago/Turabian Style

Moeini, Mohammadreza; Shojaeizadeh, Ali; Geza, Mengistu. 2021. "Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds" Water 13, no. 2: 147. https://doi.org/10.3390/w13020147

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