Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

doi:10.1007/s11783-023-1655-7

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (5) : 55 https://doi.org/10.1007/s11783-023-1655-7

RESEARCH ARTICLE

Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

Yirong Hu^1,³, Wenjie Du^2,³, Cheng Yang¹, Yang Wang²(

), Tianyin Huang⁴, Xiaoyi Xu⁴, Wenwei Li^1,³(

)

¹. CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, China
². School of Software Engineering, University of Science and Technology of China, Hefei 230026, China
³. USTC-CityU Joint Advanced Research Center, Suzhou Institute for Advanced Research of USTC, Suzhou 215123, China
⁴. National and Local Joint Engineering Laboratory for Municipal Sewage Resource Utilization Technology, Suzhou University of Science and Technology, Suzhou 215009, China

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Abstract

● A machine learning model was used to identify lake nutrient pollution sources.

● XGBoost model showed the best performance for lake water quality prediction.

● Model feature size was reduced by screening the key features with the MIC method.

● TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources.

● Next-month lake TN and TP concentrations were predicted accurately.

Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R² of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication.

Keywords Eutrophication Machine learning Water quality Nutrients Prediction

Corresponding Author(s): Yang Wang,Wenwei Li

Issue Date: 30 November 2022

Cite this article:

Yirong Hu,Wenjie Du,Cheng Yang, et al. Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique[J]. Front. Environ. Sci. Eng., 2023, 17(5): 55.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1655-7
https://academic.hep.com.cn/fese/EN/Y2023/V17/I5/55

Fig.1 Flow diagram of model development procedures for key feature identification and pollution prediction.

Fig.2 Heatmap showing MIC values between all features.

Fig.3 MIC feature screening results. There is a high correlation between the pair of features connected by arrows. Asterisk indicates the final retained features among the screened highly correlated features.

Fig.4 Prediction performances of the six ML models. Eighty percent of the 156 samples are randomly selected as the training set, and the remaining 20 % of the samples are used as the test set. (a) TN prediction results of the 6 ML models; (b) TP prediction results of the 6 ML models.

Fig.5 Boxplots of evaluation metrics for 500 runs of the six ML models, with indicators of R², RMSE, and MAPE for (a) TN and (b) TP prediction. The two outermost horizontal lines represent the outer limit, and the box represents the range between the upper quartile and the lower quartile. The horizontal line inside the box represents the median, the center circle represents the mean, and the cross represents outliers.

Fig.6 Feature importance ranking for TN and TP predictions output by XGBoost. (a) Feature weights and ranking for TN prediction; (b) Feature weights and ranking for TP prediction.

Fig.7 PDP illustrating the (a, b) lake TN and (c, d) lake TP concentrations in response to variations for different features: (a) NH₃-N_LT and COD_LT; (b) NH₃-N_JSIR and NH₃-N_WD; (c) COD_LT and SS_LT; (d) DO_JSIR and TN_JSIR.

Fig.8 Evaluation metrics for models with different sliding time window lengths for (a) TN and (b) TP prediction. Star symbol indicates the optimal sliding time window length.

Fig.9 Lake TN (a) and TP (b) prediction results of the optimized model.

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