Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models
The relationship between total phosphorus (TP) and benthic chlorophyll-a (chl-a), a vital indicator of algal biomass in freshwater ecosystems, has been well-established since the 1960s. Different machine learning models have been used to predict the benthic chl-a from the TP and dissolved P (DP), ho...
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124005326 |
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| author | Yuting Wang Sangar Khan Zongwei Lin Xinxin Qi Kamel M. Eltohamy Collins Oduro Chao Gao Paul J. Milham Naicheng Wu |
| author_facet | Yuting Wang Sangar Khan Zongwei Lin Xinxin Qi Kamel M. Eltohamy Collins Oduro Chao Gao Paul J. Milham Naicheng Wu |
| author_sort | Yuting Wang |
| collection | DOAJ |
| description | The relationship between total phosphorus (TP) and benthic chlorophyll-a (chl-a), a vital indicator of algal biomass in freshwater ecosystems, has been well-established since the 1960s. Different machine learning models have been used to predict the benthic chl-a from the TP and dissolved P (DP), however, to the best of our knowledge, colloidal and particulate P (CP and PP) have never been used in predictive models for benthic chl-a. To address this gap, we applied two machine learning algorithms—random forest (RF), and artificial neural networks (ANN) to predict benthic chl-a concentrations by incorporating these specific P fractions as separate variables. Additionally, support vector regression (SVR) was used to predict chl-a concentrations across upstream, midstream, and downstream sections. A total of 125 freshwater samples were collected from these sections of the Thousand Island Lake (TIL) watershed for analysis. The RF model (R2 = 0.88, RMSE = 2.20) outperformed the ANN (R2 = 0.37, RMSE = 4.78). The SHapley Additive exPlanations (SHAP) were used to interpret the RF model, revealing CP as the most influential predictor of benthic chl-a levels. Lower concentrations of PP and DP significantly contributed to benthic chl-a predictions, suggesting possibly rapid biogeochemical transformations among these P fractions. The SVR analysis demonstrated the dominance of DP and CP upstream and downstream, respectively, while PP was more influential in the middle stream areas. This study highlights the differential impacts of P fractions on benthic chl-a and offers new insights into aquatic health assessments in the TIL watershed. |
| format | Article |
| id | doaj-art-ac98fa3d0379414a9c9b6f8035426db3 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-ac98fa3d0379414a9c9b6f8035426db32025-01-19T06:24:44ZengElsevierEcological Informatics1574-95412025-03-0185102990Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning modelsYuting Wang0Sangar Khan1Zongwei Lin2Xinxin Qi3Kamel M. Eltohamy4Collins Oduro5Chao Gao6Paul J. Milham7Naicheng Wu8Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaKey Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resources Sciences, Zhejiang University, Hangzhou 310058, China; Department of Water Relations & Field Irrigation, National Research Centre, Dokki, Cairo 12622, EgyptDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, ChinaHawkesbury Institute for the Environment, University of Western Sydney, LB 1797, Penrith, New South Wales 2751, AustraliaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China; Corresponding author.The relationship between total phosphorus (TP) and benthic chlorophyll-a (chl-a), a vital indicator of algal biomass in freshwater ecosystems, has been well-established since the 1960s. Different machine learning models have been used to predict the benthic chl-a from the TP and dissolved P (DP), however, to the best of our knowledge, colloidal and particulate P (CP and PP) have never been used in predictive models for benthic chl-a. To address this gap, we applied two machine learning algorithms—random forest (RF), and artificial neural networks (ANN) to predict benthic chl-a concentrations by incorporating these specific P fractions as separate variables. Additionally, support vector regression (SVR) was used to predict chl-a concentrations across upstream, midstream, and downstream sections. A total of 125 freshwater samples were collected from these sections of the Thousand Island Lake (TIL) watershed for analysis. The RF model (R2 = 0.88, RMSE = 2.20) outperformed the ANN (R2 = 0.37, RMSE = 4.78). The SHapley Additive exPlanations (SHAP) were used to interpret the RF model, revealing CP as the most influential predictor of benthic chl-a levels. Lower concentrations of PP and DP significantly contributed to benthic chl-a predictions, suggesting possibly rapid biogeochemical transformations among these P fractions. The SVR analysis demonstrated the dominance of DP and CP upstream and downstream, respectively, while PP was more influential in the middle stream areas. This study highlights the differential impacts of P fractions on benthic chl-a and offers new insights into aquatic health assessments in the TIL watershed.http://www.sciencedirect.com/science/article/pii/S1574954124005326Total phosphorusFreshwater ecosystemDissolved phosphorusPredictive modelsBenthic chlorophyll-a |
| spellingShingle | Yuting Wang Sangar Khan Zongwei Lin Xinxin Qi Kamel M. Eltohamy Collins Oduro Chao Gao Paul J. Milham Naicheng Wu Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models Ecological Informatics Total phosphorus Freshwater ecosystem Dissolved phosphorus Predictive models Benthic chlorophyll-a |
| title | Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models |
| title_full | Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models |
| title_fullStr | Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models |
| title_full_unstemmed | Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models |
| title_short | Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models |
| title_sort | effect of phosphorus fractions on benthic chlorophyll a insight from the machine learning models |
| topic | Total phosphorus Freshwater ecosystem Dissolved phosphorus Predictive models Benthic chlorophyll-a |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124005326 |
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