Supervised Machine Learning for Estimation of Total Suspended Solids in Urban Watersheds
نویسندگان
چکیده
Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable (VW-kNN), Support Vector (SVR), Artificial Neural Network (ANN), Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate feasibility approaches estimation Total Suspended Solids (TSS) using national stormwater quality database. Six factors were used as features train with TSS concentration target parameter: Drainage area, land use, percent imperviousness, rainfall depth, runoff volume, antecedent dry days. Comparisons among methods demonstrated a higher degree variability in model performance, coefficient determination (R2) Nash–Sutcliffe (NSE) values ranging from 0.15 0.77. The Root Mean Square (RMSE) ranged 110 mg/L 220 mg/L. best fit was obtained AdB RF models, R2 0.77 0.74 training step 0.67 0.64 step. NSE 0.76 0.72 0.62 predictions sensitive all six factors. However, sensitivity level variable.
منابع مشابه
Estimation of total suspended solids concentration by hyperspectral remote sensing in Liaodong Bay
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ژورنال
عنوان ژورنال: Water
سال: 2021
ISSN: ['2073-4441']
DOI: https://doi.org/10.3390/w13020147