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

Wang, Wei; Hu, Xinglu; Meng, Hongzhi; Liu, Chuankun; Wang, Yang; Jiao, Tong; Chang, Qixin; Lai, Bo

doi:10.3390/d18050282

Open AccessArticle

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

by

Wei Wang

^1,2,*,

Xinglu Hu

^3,4,

Hongzhi Meng

^3,4,*,

Chuankun Liu

²,

Yang Wang

²,

Tong Jiao

^3,4,

Qixin Chang

^3,4

and

Bo Lai

¹

College of Architecture & Environment, Sichuan University, Chengdu 610065, China

²

Sichuan Academy of Environmental Policy and Planning, Chengdu 610000, China

³

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China

⁴

College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China

^*

Authors to whom correspondence should be addressed.

Diversity 2026, 18(5), 282; https://doi.org/10.3390/d18050282

Submission received: 20 April 2026 / Revised: 6 May 2026 / Accepted: 6 May 2026 / Published: 9 May 2026

(This article belongs to the Special Issue Freshwater Biodiversity Conservation and Ecological Restoration in Human-Dominated Landscapes)

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Abstract

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_Cr > 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.

Keywords: algal proliferation; high-frequency monitoring; random forest; SHAP analysis; human-dominated landscape; Fuxi River; explainable AI

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MDPI and ACS Style

Wang, W.; Hu, X.; Meng, H.; Liu, C.; Wang, Y.; Jiao, T.; Chang, Q.; Lai, B. Machine Learning-Based Prediction and Interpretability Analysis of Chlorophyll-a and Algal Density Using High-Frequency Water Quality Data. Diversity 2026, 18, 282. https://doi.org/10.3390/d18050282

AMA Style

Wang W, Hu X, Meng H, Liu C, Wang Y, Jiao T, Chang Q, Lai B. Machine Learning-Based Prediction and Interpretability Analysis of Chlorophyll-a and Algal Density Using High-Frequency Water Quality Data. Diversity. 2026; 18(5):282. https://doi.org/10.3390/d18050282

Chicago/Turabian Style

Wang, Wei, Xinglu Hu, Hongzhi Meng, Chuankun Liu, Yang Wang, Tong Jiao, Qixin Chang, and Bo Lai. 2026. "Machine Learning-Based Prediction and Interpretability Analysis of Chlorophyll-a and Algal Density Using High-Frequency Water Quality Data" Diversity 18, no. 5: 282. https://doi.org/10.3390/d18050282

APA Style

Wang, W., Hu, X., Meng, H., Liu, C., Wang, Y., Jiao, T., Chang, Q., & Lai, B. (2026). Machine Learning-Based Prediction and Interpretability Analysis of Chlorophyll-a and Algal Density Using High-Frequency Water Quality Data. Diversity, 18(5), 282. https://doi.org/10.3390/d18050282

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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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