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.
|Title||Machine learning for understanding inland water quantity, quality, and ecology|
|Authors||Alison P. Appling, Samantha K. Oliver, Jordan Read, Jeffrey Michael Sadler, Jacob Aaron Zwart|
|Publication Type||Book Chapter|
|Publication Subtype||Book Chapter|
|Record Source||USGS Publications Warehouse|
|USGS Organization||Wisconsin Water Science Center; Office of Water Information; WMA - Integrated Information Dissemination Division|