Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?. Issue 4 (24th April 2022) - Varadharajan, Charuleka; Appling, Alison P.; Arora, Bhavna; Christianson, Danielle S.; Hendrix, Valerie C.; Kumar, Vipin; Lima, Aranildo R.; Müller, Juliane; Oliver, Samantha; Ombadi, Mohammed; Perciano, Talita; Sadler, Jeffrey M; et al.
Abstract: The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality. Abstract : Machine learning (ML) is being increasingly used for hydrological applications and has the potential to improve predictive capabilities and decipher complex, diverse human‐natural processes impacting water quality. In this paper, we review relevant state‐of‐the art models and present considerations for using ML and its limitations when applied for water quality problems. We then discuss opportunities to improve ML models using emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge and complex data, explainable AI, uncertainty quantification, and model‐data integration.