Journal
HYDROLOGICAL PROCESSES
Volume 36, Issue 4, Pages -Publisher
WILEY
DOI: 10.1002/hyp.14565
Keywords
analysis; machine learning; models; predictions; rivers; streams; water quality
Categories
Funding
- U.S. Department of Energy, Office of Science, Biological and Environmental Research [DE-AC02-05CH11231]
- National Science Foundation
- Directorate for Computer and Information Science and Engineering [1934721]
- Lawrence Berkeley National Laboratory
- U.S. Geological Survey
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The global decline in water quality in rivers and streams has created an urgent need for new watershed management strategies. Machine learning can aid in developing more accurate, computationally tractable, and scalable models for analyzing and predicting river water quality. When combined with decades of process understanding, machine learning has the potential to address water quality problems effectively.
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.
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