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Machine Learning-Based Prediction and Interpretability Analysis of Chlorophyll-a and Algal Density Using High-Frequency Water Quality Data.

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Τίτλος: Machine Learning-Based Prediction and Interpretability Analysis of Chlorophyll-a and Algal Density Using High-Frequency Water Quality Data.
Συγγραφείς: Wang, Wei, Hu, Xinglu, Meng, Hongzhi, Liu, Chuankun, Wang, Yang, Jiao, Tong, Chang, Qixin, Lai, Bo
Πηγή: Diversity (14242818); May2026, Vol. 18 Issue 5, p282, 19p
Θεματικοί όροι: Chlorophyll, Algal populations, Shapley Additive Explanations, Ecosystems, Water quality monitoring, Machine learning, Freshwater ecology, Random forest algorithms
Γεωγραφικοί όροι: China
Περίληψη: Rapid algal proliferation in human-impacted freshwater ecosystems necessitates advanced predictive tools for effective management. This study aims to capture the stochastic dynamics of algal blooms in the Fuxi River, China, using high-frequency monitoring and interpretable machine learning. A 2 h interval dataset was utilized to construct Random Forest models in Python for predicting Chlorophyll-a (Chl-a) and algal density, both measured via in situ multi-wavelength fluorescence. Model interpretability was achieved through SHAP (SHapley Additive exPlanations) analysis to identify non-linear environmental drivers and ecological thresholds. The models demonstrated high predictive accuracy. SHAP analysis revealed that dissolved oxygen (>10 mg/L) is the primary diagnostic indicator for peak Chl-a, with an optimal thermal window of 15–20 °C identified for proliferation. For algal density, chemical oxygen demand (CODCr > 25 mg/L) and conductivity (>1000 μS/cm) were identified as critical tipping points, showing pronounced synergistic effects between organic enrichment and nutrient levels. This study underscores that managing organic loading and monitoring specific thermal–hydrochemical windows are vital for mitigating extreme algal events, providing a robust, interpretable framework for real-time water quality early warning. [ABSTRACT FROM AUTHOR]
Copyright of Diversity (14242818) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Machine Learning-Based Prediction and Interpretability Analysis of Chlorophyll-a and Algal Density Using High-Frequency Water Quality Data.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Wei%22">Wang, Wei</searchLink><br /><searchLink fieldCode="AR" term="%22Hu%2C+Xinglu%22">Hu, Xinglu</searchLink><br /><searchLink fieldCode="AR" term="%22Meng%2C+Hongzhi%22">Meng, Hongzhi</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Chuankun%22">Liu, Chuankun</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yang%22">Wang, Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Jiao%2C+Tong%22">Jiao, Tong</searchLink><br /><searchLink fieldCode="AR" term="%22Chang%2C+Qixin%22">Chang, Qixin</searchLink><br /><searchLink fieldCode="AR" term="%22Lai%2C+Bo%22">Lai, Bo</searchLink>
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  Data: Diversity (14242818); May2026, Vol. 18 Issue 5, p282, 19p
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  Data: <searchLink fieldCode="DE" term="%22Chlorophyll%22">Chlorophyll</searchLink><br /><searchLink fieldCode="DE" term="%22Algal+populations%22">Algal populations</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22Ecosystems%22">Ecosystems</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality+monitoring%22">Water quality monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Freshwater+ecology%22">Freshwater ecology</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
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  Data: Rapid algal proliferation in human-impacted freshwater ecosystems necessitates advanced predictive tools for effective management. This study aims to capture the stochastic dynamics of algal blooms in the Fuxi River, China, using high-frequency monitoring and interpretable machine learning. A 2 h interval dataset was utilized to construct Random Forest models in Python for predicting Chlorophyll-a (Chl-a) and algal density, both measured via in situ multi-wavelength fluorescence. Model interpretability was achieved through SHAP (SHapley Additive exPlanations) analysis to identify non-linear environmental drivers and ecological thresholds. The models demonstrated high predictive accuracy. SHAP analysis revealed that dissolved oxygen (>10 mg/L) is the primary diagnostic indicator for peak Chl-a, with an optimal thermal window of 15–20 °C identified for proliferation. For algal density, chemical oxygen demand (COD<subscript>Cr</subscript> > 25 mg/L) and conductivity (>1000 μS/cm) were identified as critical tipping points, showing pronounced synergistic effects between organic enrichment and nutrient levels. This study underscores that managing organic loading and monitoring specific thermal–hydrochemical windows are vital for mitigating extreme algal events, providing a robust, interpretable framework for real-time water quality early warning. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Diversity (14242818) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/d18050282
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      – Code: eng
        Text: English
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        PageCount: 19
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        Type: general
      – SubjectFull: Chlorophyll
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      – SubjectFull: Algal populations
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      – SubjectFull: Shapley Additive Explanations
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      – SubjectFull: Machine learning
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      – SubjectFull: Freshwater ecology
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      – SubjectFull: Random forest algorithms
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              Text: May2026
              Type: published
              Y: 2026
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