A Review of Machine Learning in Snow Water Equivalent Monitoring

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Authors

Faye Hsu, Ziheng Sun, Gokul Prathin, Sanjana Achan

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 08:19

Last Updated: 2024-05-09 15:19

License

CC BY Attribution 4.0 International