Utilizing machine learning models to grasp water quality dynamic changes in lake eutrophication through phytoplankton parameters

Highlights

	● Accurate identification of lake eutrophication was achieved via ML models.
	● XGBoost model has superior performance in identifying limiting nutrients.
	● LightGBM model effectively uses phytoplankton for water quality characterization.
	● ML model with TN/TP ratio and phytoplankton can track lake eutrophication dynamics.

Abstract

Phytoplankton serve as vital indicators of eutrophication levels. However, relying solely on phytoplankton parameters, such as chlorophyll-a, limits our comprehensive understanding of the intricate eutrophication conditions in natural lakes, particularly in terms of timely analysis of changes in limiting nutrients and their concentrations. This study presents machine learning (ML) models for predicting and identifying lake eutrophication. Five tree-based ML models were developed using the latest data on hydrological, water quality, and meteorological parameters obtained from 34 sites in the Huating Lake basin over 5 months. The extreme gradient boosting model exhibited high accuracy in predicting the total nitrogen/total phosphorus ratio (TN/TP) (R² = 0.88; RMSE = 24.60; MAPE = 26.14%). Analysis of the TN/TP ratio and output eigenvalue weight revealed that phosphorus plays a crucial role in eutrophication, probably because of the low-flow and deep-water characteristics of the basin. Furthermore, the light gradient boosting machine model exhibited outstanding performance and high accuracy in predicting phytoplankton parameters, especially the Shannon index (H′) (R² = 0.92; RMSE = 0.11; MAPE = 4.95%). The mesotrophic classification of the Huating Lake determined using the H′ threshold, coincided with the findings from the H′ analysis. Future research should cover a wider range of pollution sources and spatiotemporal dimensions to further validate our findings. Overall, this study highlights the potential of incorporating the TN/TP ratio and phytoplankton parameters into ML techniques for effective monitoring and management of environmental conditions.

Graphical abstract

Keywords

Machine learning / Lake / Phytoplankton / Water quality

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Nos. 51278001 and U22A20401), and the Anhui Province Major Science and Technology Projects (China) (No. 202003a0702014) for supporting this work. We thank Letpub for its linguistic assistance during the preparation of this manuscript.

Conflict of Interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-025-1934-6 and is accessible for authorized users.

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