Machine learning for understanding inland water quantity, quality, and ecology

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/B978-0-12-819166-8.00121-3. This is version 1 of this Preprint.

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Authors

Alison Paige Appling , Samantha Kay Oliver , Jordan S. Read, Jeffrey Michael Sadler , Jacob Zwart 

Abstract

This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring hypotheses; estimating physically or biologically meaningful parameters for use in further modeling; and revealing patterns in complex, multidimensional data or model outputs. An important research frontier is the injection of limnological knowledge into machine-learning models, which has shown great promise for increasing such models’ accuracy, trustworthiness, and interpretability. Here we describe a few of the most powerful machine learning tools, describe best practices for employing these tools and injecting knowledge guidance, and give examples of their applications to advance understanding of inland waters.

DOI

https://doi.org/10.31223/X5964S

Subjects

Applied Statistics, Fresh Water Studies, Hydrology

Keywords

machine learning, Neural Networks, Deep learning, classification and regression trees, clustering, dimensionality reduction, data mining, Artificial Intelligence

Dates

Published: 2022-09-03 19:01

License

CC0 1.0 Universal - Public Domain Dedication

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