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Deep Learning for Water Quality
Citation:
Zhi, W., A. Appling, H. Golden, J. Podgorski, AND L. Li. Deep Learning for Water Quality. Nature Water. Nature Portfolio, Berlin, Germany, 2:228–241, (2024). https://doi.org/10.1038/s44221-024-00202-z
Impact/Purpose:
Advanced methods for understanding and forecasting future water quality conditions are imperative. We propose that deep learning is a promising and currently underutilized option for water quality modeling, particularly due to its strength in discovering intricate structures and relationships in high-dimensional data. Here we offer a succinct overview of the inner workings of deep learning and its potential for transforming water quality prediction by highlighting its strengths and weaknesses, contrasting it to traditional approaches. We examine the opportunities for deep learning in addressing data scarcity and revealing the underlying drivers of water quality dynamics. We advance a forward-looking perspective on the future of water quality prediction, in which the prominent role of deep learning is both inevitable and essential.
Description:
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.
