Comprehensive assessment of the water environment carrying capacity based on machine learning.

Human activities and climate change are constantly threatening water environment systems. To alleviate pressure on the water environment and meet the requirements for sustainable societal development, it has become important to study the water environment carrying capacity (WECC). As science and technology have advanced, the use of artificial intelligence has penetrated various disciplines, and the application of machine learning has achieved many good results in the environmental sciences; however, little attention has been given to the WECC. To this end, a WECC assessment model was developed based on a variety of machine learning algorithms, to provide a novel approach and better understanding of the WECC. In this study, 13 natural and anthropogenic factors related to the water environment system in Liaoning Province (China) were selected, and the study period ranged from 2015 to 2019. Four basic machine learning algorithms, namely, random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and k-nearest neighbour (KNN), were used for batch modelling, and stacking was carried out based on the output results. A comparison and screening of the models revealed that RF and the stacking model had the best model prediction performances. Because RF was relatively simple and convenient, a comprehensive assessment of the regional WECC based on this algorithm was carried out. The results showed that most of the control units with an overloaded WECC are located in the central and northern regions of Liaoning Province, and the control units of overloading can be well identified and the trend of regional WECC status can be observed. To improve model transparency, we also performed an explanatory analysis based on the shapley additive explanations (SHAP). The results showed that precipitation, soil texture, sewage treatment plant, wind speed, and temperature were the most important factors influencing the water environment system. Precipitation is the most influential factor, and with increasing rainfall, the WECC first improves and then gradually deteriorates. For the anthropogenic factors, the WECC generally improves first and then worsens as these factors increase. These research results can be used to provide a powerful and practical new methodology for the comprehensive assessment of the WECC and provide a basis for the development of water environment policies and regulatory implementation in the region. [Display omitted] • A regional WECC assessment method based on machine learning was developed. • Batch modelling and model stacking were performed via machine learning. • Model explanation was based on the SHAP analysis method. • Results of the assessment are critical to the management of water environmental systems. • Machine learning is a promising method for assessing the WECC. [ABSTRACT FROM AUTHOR]