Application of machine learning to surface water quality management
Machine learning,a key subfield of artificial intelligence,has been playing an increasingly important role in the environmental field.When dealing with complex problems in surface water quality management,it shows significant advantages over traditional methods.This review focused on the applications of various machine learning algorithms in surface water quality management.It analyzed the effects of different water quality parameters,such as dissolved oxygen,biological oxygen demand,chemical oxygen demand,turbidity,temperature,pH,etc.,for surface water quality classification,monitoring,and prediction.This review also provided an in-depth discussion of several machine learning models that were commonly used in real-world engineering applications,such as artificial neural networks,support vector machines,random forests,decision trees,and deep learning.In addition,this review explored the application of hybrid models for improving output accuracy in surface water quality management.In summary,the realization of machine learning for accurate and efficient management of surface water quality not only depends on suitability of selected parameters for specific algorithms but also relies on reasonable use of multiple machine learning models to increase the credibility of the output results.
machine learningenvironmental engineeringwater quality managementsurface water
国家科技期刊平台
NSTL
万方数据
