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Abstract
In recent years, the scientific community focused on snow dynamics has witnessed a surge in efforts aimed at enhancing Snow Water Equivalent (SWE) monitoring capabilities, largely propelled by the incorporation of Machine Learning (ML) techniques. This comprehensive review delves into the current state of research within this evolving domain, shedding light on the indispensable role of precise SWE predictions in bolstering water resource management strategies and fostering environmental resilience amidst the backdrop of climate variability. By critically examining existing literature, this review underscores the imperative nature of ML-based methodologies in overcoming the inherent limitations of traditional monitoring paradigms. Highlighting the adaptability and promise of various ML algorithms, this paper serves as a cornerstone resource for researchers, practitioners, and policymakers dedicated to advancing SWE estimation practices and, consequently, promoting sustainable water resource management in snow-dominated regions.
DOI
https://doi.org/10.31223/X57391
Subjects
Artificial Intelligence and Robotics, Computer Sciences, Databases and Information Systems, Earth Sciences, Hydrology, Numerical Analysis and Scientific Computing, Oceanography and Atmospheric Sciences and Meteorology
Keywords
snow water equivalent, machine learning, numeric model, SNOTEL, SWE
Dates
Published: 2024-05-09 11:19
Last Updated: 2024-05-09 18:19
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