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Article summary:
1. The global decline of water quality in rivers and streams has resulted in a need for new watershed management strategies.
2. Machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality.
3. ML combined with decades of process understanding can help address fundamental science questions and enable decision-relevant predictions of riverine water quality.
Article analysis:
The article is generally trustworthy and reliable, as it provides an overview of the current state-of-the-art applications of machine learning (ML) for water quality models, as well as potential opportunities to improve the use of ML with emerging computational and mathematical methods. It also discusses considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs.
The article does not appear to be biased or one-sided in its reporting; it presents both sides equally by discussing both the benefits of using ML for water quality models as well as potential challenges that may arise from its use. It also provides evidence to support its claims by citing relevant research studies throughout the text.
The article does not appear to contain any promotional content or partiality; instead, it provides an objective overview of the current state-of-the-art applications of ML for water quality models, as well as potential opportunities to improve the use of ML with emerging computational and mathematical methods.
The article does not appear to contain any missing points of consideration or unexplored counterarguments; instead, it provides a comprehensive overview of the current state-of-the-art applications of ML for water quality models, as well as potential opportunities to improve the use of ML with emerging computational and mathematical methods.
Finally, the article does note possible risks associated with using ML for water quality models; it discusses considerations such as scale and complexity, available data and computational resources, stakeholder needs, etc., which must be taken into account when using ML for this purpose.