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Abstract. Water quality in lakes is an emergent property of complex biotic and abiotic processes that differ across spatial and temporal scales. Water quality is also a determinant of ecosystem services that lakes provide and is thus of great interest to ecologists. Machine learning and other computer science techniques are increasingly being used to predict water quality dynamics as well as to gain a greater understanding of water quality patterns and controls. To benefit the sciences of both ecology and computer science, we have created a benchmark dataset of lake water quality time series and vertical profiles. LakeBeD-US contains over 500 million unique observations of lake water quality collected by multiple long-term monitoring programs across 17 water quality variables from 21 lakes in the United States. There are two published versions of LakeBeD-US: the “Ecology Edition” published in the Environmental Data Initiative repository (https://doi.org/10.6073/pasta/c56a204a65483790f6277de4896d7140, McAfee et al., 2024) and the “Computer Science Edition” published in the Hugging Face repository (https://doi.org/10.57967/hf/3771, Pradhan et al., 2024). Each edition is formatted in a manner conducive to inquiries and analyses specific to each domain. For ecologists, LakeBeD-US: Ecology Edition provides an opportunity to study the spatial and temporal dynamics of several lakes with varying water quality, ecosystem, and landscape characteristics. For computer scientists, LakeBeD-US: Computer Science Edition acts as a benchmark dataset that enables the advancement of machine learning for water quality prediction.
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This content will become publicly available on January 1, 2026
Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources
Abstract Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world’s freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread predictions of hydrological variables such as river flow and water quality has become increasingly urgent due to climate and land use change over the past decades, and their associated impacts on water resources. Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. We review relevant state-of-the art applications of machine learning for streamflow, water quality, and other water resources prediction and discuss opportunities to improve the use of machine learning with emerging methods for incorporating watershed characteristics and process knowledge into classical, deep learning, and transfer learning methodologies. The analysis here suggests most prior efforts have been focused on deep learning frameworks built on many sites for predictions at daily time scales in the United States, but that comparisons between different classes of machine learning methods are few and inadequate. We identify several open questions for time series predictions in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding and spatial context, and explainable AI techniques in modern machine learning frameworks.
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- PAR ID:
- 10592409
- Publisher / Repository:
- Cambridge University Press
- Date Published:
- Journal Name:
- Environmental Data Science
- Volume:
- 4
- ISSN:
- 2634-4602
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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