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

By: , and 
Edited by: Thomas Mehner and Klement Tockner



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.

Publication type Book chapter
Publication Subtype Book Chapter
Title Machine learning for understanding inland water quantity, quality, and ecology
DOI 10.1016/B978-0-12-819166-8.00121-3
Edition 2
Volume 4
Year Published 2022
Language English
Publisher Elsevier
Contributing office(s) Wisconsin Water Science Center, Office of Water Information, WMA - Integrated Information Dissemination Division
Description 22 p.
Larger Work Type Book
Larger Work Subtype Monograph
Larger Work Title Encyclopedia of inland waters
First page 585
Last page 606
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