Importance of land use factors in the prediction of water quality of the Upper Green River watershed, Kentucky, USA, using random forest.
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| Title: | Importance of land use factors in the prediction of water quality of the Upper Green River watershed, Kentucky, USA, using random forest. |
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| Authors: | Venkateswarlu, Turuganti1 (AUTHOR), Anmala, Jagadeesh2 (AUTHOR) jagadeesh@hyderabad.bits-pilani.ac.in |
| Source: | Environment, Development & Sustainability. Sep2024, Vol. 26 Issue 9, p23961-23984. 24p. |
| Subject Terms: | *Artificial neural networks, *Urban land use, *Forests & forestry, *Land surface temperature, *Random forest algorithms |
| Abstract: | Surface waters are essential for meeting the needs of the world. In many regions, stream water quality is a major concern due to contamination from multiple sources. Stream water is also susceptible to climatic events and land-use practices influencing its catchment. Understanding the impact of such events on stream water quality is crucial for managing and protecting aquatic ecosystems and providing safe drinking water to communities that rely on these streams. Hence, monitoring and evaluating stream water quality holds significance in identifying potential hazards and implementing suitable management strategies. In this paper, a novel effort was made to determine the relative feature importance of a set of watershed characteristics (precipitation, temperature, urban land use, agricultural land use, and forest land-use factors) on four important water quality parameters (WQPs): fecal coliforms (FC), turbidity, pH, and conductivity of the Upper Green River watershed, Kentucky, USA. Random forest (RF), an ensemble learning method, was used to predict the WQPs from the causal parameters and determine the feature importance characteristics of the four WQPs previously mentioned. This model demonstrated that precipitation and temperature are the most influential factors on FC, turbidity, and pH. Forest land use and temperature are the two most important factors for conductivity. The novel feature importance factors of the RF model have likewise been confirmed for each WQP. In modeling stream WQPs, the developed the RF model outperformed the artificial neural network (ANN) model. Using the RF model, we obtain regression coefficients of (0.93, 0.74, and 0.94) for pH in training, testing, and overall. We obtain regression coefficients of (0.60, 0.64, and 0.61) using the ANN model. Overall, the RF model was more effective than the ANN model in modeling stream WQPs. The model identified precipitation and temperature as the most influential factors on FC, turbidity, and pH, while forest land use and temperature were the most important factors in determining conductivity. It is also found that land use factors are important to improve the accuracy of WQPs predictions from climate variables. The results of this study can be used by authorities to better understand and control pollution at the watershed scale. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 179166166 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Importance of land use factors in the prediction of water quality of the Upper Green River watershed, Kentucky, USA, using random forest. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Venkateswarlu%2C+Turuganti%22">Venkateswarlu, Turuganti</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Anmala%2C+Jagadeesh%22">Anmala, Jagadeesh</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jagadeesh@hyderabad.bits-pilani.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Sep2024, Vol. 26 Issue 9, p23961-23984. 24p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Urban+land+use%22">Urban land use</searchLink><br />*<searchLink fieldCode="DE" term="%22Forests+%26+forestry%22">Forests & forestry</searchLink><br />*<searchLink fieldCode="DE" term="%22Land+surface+temperature%22">Land surface temperature</searchLink><br />*<searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Surface waters are essential for meeting the needs of the world. In many regions, stream water quality is a major concern due to contamination from multiple sources. Stream water is also susceptible to climatic events and land-use practices influencing its catchment. Understanding the impact of such events on stream water quality is crucial for managing and protecting aquatic ecosystems and providing safe drinking water to communities that rely on these streams. Hence, monitoring and evaluating stream water quality holds significance in identifying potential hazards and implementing suitable management strategies. In this paper, a novel effort was made to determine the relative feature importance of a set of watershed characteristics (precipitation, temperature, urban land use, agricultural land use, and forest land-use factors) on four important water quality parameters (WQPs): fecal coliforms (FC), turbidity, pH, and conductivity of the Upper Green River watershed, Kentucky, USA. Random forest (RF), an ensemble learning method, was used to predict the WQPs from the causal parameters and determine the feature importance characteristics of the four WQPs previously mentioned. This model demonstrated that precipitation and temperature are the most influential factors on FC, turbidity, and pH. Forest land use and temperature are the two most important factors for conductivity. The novel feature importance factors of the RF model have likewise been confirmed for each WQP. In modeling stream WQPs, the developed the RF model outperformed the artificial neural network (ANN) model. Using the RF model, we obtain regression coefficients of (0.93, 0.74, and 0.94) for pH in training, testing, and overall. We obtain regression coefficients of (0.60, 0.64, and 0.61) using the ANN model. Overall, the RF model was more effective than the ANN model in modeling stream WQPs. The model identified precipitation and temperature as the most influential factors on FC, turbidity, and pH, while forest land use and temperature were the most important factors in determining conductivity. It is also found that land use factors are important to improve the accuracy of WQPs predictions from climate variables. The results of this study can be used by authorities to better understand and control pollution at the watershed scale. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10668-023-03630-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 23961 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Urban land use Type: general – SubjectFull: Forests & forestry Type: general – SubjectFull: Land surface temperature Type: general – SubjectFull: Random forest algorithms Type: general Titles: – TitleFull: Importance of land use factors in the prediction of water quality of the Upper Green River watershed, Kentucky, USA, using random forest. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Venkateswarlu, Turuganti – PersonEntity: Name: NameFull: Anmala, Jagadeesh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1387585X Numbering: – Type: volume Value: 26 – Type: issue Value: 9 Titles: – TitleFull: Environment, Development & Sustainability Type: main |
| ResultId | 1 |
