Effects of Antecedent Precipitation Amount and COVID-19 Lockdown on Water Quality along an Urban Gradient
Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Daniel Ramirez [verfasserIn] Heejun Chang [verfasserIn] Katherine Gelsey [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Hydrology - MDPI AG, 2015, 9(2022), 12, p 220 |
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| Übergeordnetes Werk: |
volume:9 ; year:2022 ; number:12, p 220 |
| Links: |
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| DOI / URN: |
10.3390/hydrology9120220 |
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| Katalog-ID: |
DOAJ083160191 |
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| 520 | |a Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect <i<E. coli</i<, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and <i<E. coli</i<, lead, zinc, and TSS concentrations. Road length is positively associated with <i<E. coli</i< and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R<sup<2</sup< = 0.58), followed by TSS (R<sup<2</sup< = 0.54) and nitrate (R<sup<2</sup< = 0.46). <i<E. coli</i< was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R<sup<2</sup<. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling. | ||
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10.3390/hydrology9120220 doi (DE-627)DOAJ083160191 (DE-599)DOAJebea470fb6c24fc0b23396b682caa386 DE-627 ger DE-627 rakwb eng Daniel Ramirez verfasserin aut Effects of Antecedent Precipitation Amount and COVID-19 Lockdown on Water Quality along an Urban Gradient 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect <i<E. coli</i<, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and <i<E. coli</i<, lead, zinc, and TSS concentrations. Road length is positively associated with <i<E. coli</i< and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R<sup<2</sup< = 0.58), followed by TSS (R<sup<2</sup< = 0.54) and nitrate (R<sup<2</sup< = 0.46). <i<E. coli</i< was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R<sup<2</sup<. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling. urban runoff machine learning model water quality temporal analysis urban runoff–management antecedent precipitation Science Q Heejun Chang verfasserin aut Katherine Gelsey verfasserin aut In Hydrology MDPI AG, 2015 9(2022), 12, p 220 (DE-627)791048659 (DE-600)2777964-6 23065338 nnns volume:9 year:2022 number:12, p 220 https://doi.org/10.3390/hydrology9120220 kostenfrei https://doaj.org/article/ebea470fb6c24fc0b23396b682caa386 kostenfrei https://www.mdpi.com/2306-5338/9/12/220 kostenfrei https://doaj.org/toc/2306-5338 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2022 12, p 220 |
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10.3390/hydrology9120220 doi (DE-627)DOAJ083160191 (DE-599)DOAJebea470fb6c24fc0b23396b682caa386 DE-627 ger DE-627 rakwb eng Daniel Ramirez verfasserin aut Effects of Antecedent Precipitation Amount and COVID-19 Lockdown on Water Quality along an Urban Gradient 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect <i<E. coli</i<, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and <i<E. coli</i<, lead, zinc, and TSS concentrations. Road length is positively associated with <i<E. coli</i< and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R<sup<2</sup< = 0.58), followed by TSS (R<sup<2</sup< = 0.54) and nitrate (R<sup<2</sup< = 0.46). <i<E. coli</i< was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R<sup<2</sup<. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling. urban runoff machine learning model water quality temporal analysis urban runoff–management antecedent precipitation Science Q Heejun Chang verfasserin aut Katherine Gelsey verfasserin aut In Hydrology MDPI AG, 2015 9(2022), 12, p 220 (DE-627)791048659 (DE-600)2777964-6 23065338 nnns volume:9 year:2022 number:12, p 220 https://doi.org/10.3390/hydrology9120220 kostenfrei https://doaj.org/article/ebea470fb6c24fc0b23396b682caa386 kostenfrei https://www.mdpi.com/2306-5338/9/12/220 kostenfrei https://doaj.org/toc/2306-5338 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2022 12, p 220 |
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10.3390/hydrology9120220 doi (DE-627)DOAJ083160191 (DE-599)DOAJebea470fb6c24fc0b23396b682caa386 DE-627 ger DE-627 rakwb eng Daniel Ramirez verfasserin aut Effects of Antecedent Precipitation Amount and COVID-19 Lockdown on Water Quality along an Urban Gradient 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect <i<E. coli</i<, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and <i<E. coli</i<, lead, zinc, and TSS concentrations. Road length is positively associated with <i<E. coli</i< and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R<sup<2</sup< = 0.58), followed by TSS (R<sup<2</sup< = 0.54) and nitrate (R<sup<2</sup< = 0.46). <i<E. coli</i< was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R<sup<2</sup<. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling. urban runoff machine learning model water quality temporal analysis urban runoff–management antecedent precipitation Science Q Heejun Chang verfasserin aut Katherine Gelsey verfasserin aut In Hydrology MDPI AG, 2015 9(2022), 12, p 220 (DE-627)791048659 (DE-600)2777964-6 23065338 nnns volume:9 year:2022 number:12, p 220 https://doi.org/10.3390/hydrology9120220 kostenfrei https://doaj.org/article/ebea470fb6c24fc0b23396b682caa386 kostenfrei https://www.mdpi.com/2306-5338/9/12/220 kostenfrei https://doaj.org/toc/2306-5338 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2022 12, p 220 |
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10.3390/hydrology9120220 doi (DE-627)DOAJ083160191 (DE-599)DOAJebea470fb6c24fc0b23396b682caa386 DE-627 ger DE-627 rakwb eng Daniel Ramirez verfasserin aut Effects of Antecedent Precipitation Amount and COVID-19 Lockdown on Water Quality along an Urban Gradient 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect <i<E. coli</i<, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and <i<E. coli</i<, lead, zinc, and TSS concentrations. Road length is positively associated with <i<E. coli</i< and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R<sup<2</sup< = 0.58), followed by TSS (R<sup<2</sup< = 0.54) and nitrate (R<sup<2</sup< = 0.46). <i<E. coli</i< was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R<sup<2</sup<. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling. urban runoff machine learning model water quality temporal analysis urban runoff–management antecedent precipitation Science Q Heejun Chang verfasserin aut Katherine Gelsey verfasserin aut In Hydrology MDPI AG, 2015 9(2022), 12, p 220 (DE-627)791048659 (DE-600)2777964-6 23065338 nnns volume:9 year:2022 number:12, p 220 https://doi.org/10.3390/hydrology9120220 kostenfrei https://doaj.org/article/ebea470fb6c24fc0b23396b682caa386 kostenfrei https://www.mdpi.com/2306-5338/9/12/220 kostenfrei https://doaj.org/toc/2306-5338 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 9 2022 12, p 220 |
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Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect <i<E. coli</i<, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and <i<E. coli</i<, lead, zinc, and TSS concentrations. Road length is positively associated with <i<E. coli</i< and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R<sup<2</sup< = 0.58), followed by TSS (R<sup<2</sup< = 0.54) and nitrate (R<sup<2</sup< = 0.46). <i<E. coli</i< was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R<sup<2</sup<. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling. |
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Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect <i<E. coli</i<, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and <i<E. coli</i<, lead, zinc, and TSS concentrations. Road length is positively associated with <i<E. coli</i< and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R<sup<2</sup< = 0.58), followed by TSS (R<sup<2</sup< = 0.54) and nitrate (R<sup<2</sup< = 0.46). <i<E. coli</i< was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R<sup<2</sup<. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling. |
| abstract_unstemmed |
Water quality is affected by multiple spatial and temporal factors, including the surrounding land characteristics, human activities, and antecedent precipitation amounts. However, identifying the relationships between water quality and spatially and temporally varying environmental variables with a machine learning technique in a heterogeneous urban landscape has been understudied. We explore how seasonal and variable precipitation amounts and other small-scale landscape variables affect <i<E. coli</i<, total suspended solids (TSS), nitrogen-nitrate, orthophosphate, lead, and zinc concentrations in Portland, Oregon, USA. Mann–Whitney tests were used to detect differences in water quality between seasons and COVID-19 periods. Spearman’s rank correlation analysis was used to identify the relationship between water quality and explanatory variables. A Random Forest (RF) model was used to predict water quality using antecedent precipitation amounts and landscape variables as inputs. The performance of RF was compared with that of ordinary least squares (OLS). Mann–Whitney tests identified statistically significant differences in all pollutant concentrations (except TSS) between the wet and dry seasons. Nitrate was the only pollutant to display statistically significant reductions in median concentrations (from 1.5 mg/L to 1.04 mg/L) during the COVID-19 lockdown period, likely associated with reduced traffic volumes. Spearman’s correlation analysis identified the highest correlation coefficients between one-day precipitation amounts and <i<E. coli</i<, lead, zinc, and TSS concentrations. Road length is positively associated with <i<E. coli</i< and zinc. The Random Forest (RF) model best predicts orthophosphate concentrations (R<sup<2</sup< = 0.58), followed by TSS (R<sup<2</sup< = 0.54) and nitrate (R<sup<2</sup< = 0.46). <i<E. coli</i< was the most difficult to model and had the highest RMSE, MAE, and MAPE values. Overall, the Random Forest model outperformed OLS, as evaluated by RMSE, MAE, MAPE, and R<sup<2</sup<. The Random Forest was an effective approach to modeling pollutant concentrations using both categorical seasonal and COVID data along with continuous rain and landscape variables to predict water quality in urban streams. Implementing optimization techniques can further improve the model’s performance and allow researchers to use a machine learning approach for water quality modeling. |
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