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Deep learning for water quality

March 12, 2024

Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.

Publication Year 2024
Title Deep learning for water quality
DOI 10.1038/s44221-024-00202-z
Authors Wei Zhi, Alison P. Appling, Heather E. Golden, Joel Podgorski, Li Li
Publication Type Article
Publication Subtype Journal Article
Series Title Nature Water
Index ID 70254335
Record Source USGS Publications Warehouse
USGS Organization WMA - Integrated Modeling and Prediction Division