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

doi:10.1007/s11783-025-1934-6

2025, Vol. 19

Issue (2) : 14 https://doi.org/10.1007/s11783-025-1934-6

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

Yong Fang, Ruting Huang(

), Yeyin Zhang, Jun Zhang, Wenni Xi, Xianyang Shi(

)

Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

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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.

Keywords Machine learning Lake Phytoplankton Water quality

Corresponding Author(s): Ruting Huang,Xianyang Shi

Issue Date: 21 November 2024

Cite this article:

Xianyang Shi,Wenni Xi,Jun Zhang, et al. Utilizing machine learning models to grasp water quality dynamic changes in lake eutrophication through phytoplankton parameters[J]. Front. Environ. Sci. Eng., 2025, 19(2): 14.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-025-1934-6
https://academic.hep.com.cn/fese/EN/Y2025/V19/I2/14

Fig.1 Flowchart of the model development and evaluation, followed by feature importance analyses.

Fig.2 Spatiotemporal variation in environmental chemical parameters (TP, TN, TN/TP, and MC) in the Huating Lake basin during June, July, and September 2022 and March 2023. (a) TP; (b) TN; (c) TN/TP; (d) MC.

Fig.3 Spatiotemporal variation in environmental biological parameters (algae cell density (ACD), H′, J, D, 1/D, and H) in the Huating Lake basin during June, July, and September 2022 and March 2023. (a) ACD; (b) H′; (c) J; (d) D; (e) 1/D; (f) H.

Fig.4 MIC values between parameters.

Fig.5 Plot of percentage evaluation metrics after 500 runs of five ML models, with R², RMSE, and MAPE metrics predicted for each target parameter (H′, J, D, H, TN/TP, and MC): (a) H′; (b) J; (c) D; (d) H; (e) TN/TP; (f) MC.

Fig.6 Feature weights and rankings based on XGBoost and LightGBM outputs for each target parameter (H′, J, D, H, TN/TP, and MC) prediction: (a) H′, (b) J, (c) D, (d) H, (e) TN/TP, and (f) MC.

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