Advancing river corridor science beyond disciplinary boundaries with an inductive approach to hypothesis generation

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Authors

Adam Scott Ward , Jennifer Drummond, Angang Li, Anna Lupon, Marie Kurz, Jay Zarnetske, James Stegen, Eugenia Marti, Valerie Ouellet, Nicolai Brekenfeld, Feng Mao, Emily Graham, Susana Bernal, Stefan Krause, David Hannah, Aaron Packman, Todd Royer, Megan Klaar

Abstract

A unified conceptual framework for river corridors requires synthesis of diverse site-, method- and discipline-specific findings. The river research community has developed a substantial body of observations and process-specific interpretations, but we are still lacking a comprehensive model to distill this knowledge into fundamental transferable concepts. We confront the challenge of how a discipline classically organized around the deductive model of systematically collecting of site-, scale-, and mechanism-specific observations begins the process of synthesis. Machine learning is particularly well-suited to inductive generation of hypotheses. In this study, we prototype an inductive approach to holistic synthesis of river corridor observations, using support vector machine regression to identify potential couplings or feedbacks that would not necessarily arise from classical approaches. This approach generated 672 relationships linking a suite of 157 variables each measured at 62 locations in a 5th order river network. Eighty four percent of these relationships have not been previously investigated, and representing potential (hypothetical) process connections. We document relationships consistent with current understanding including hydrologic exchange processes, microbial ecology, and the River Continuum Concept, supporting that the approach can identify meaningful relationships in the data. Moreover, we highlight examples of two novel research questions that stem from interpretation of inductively-generated relationships. This study demonstrates the implementation of machine learning for hypothesis generation, sieving complex data sets for a small set of candidate relationships that warrant further study, including data types not commonly measured together. This structured approach provides a means to unify the fragmented knowledge gained by traditional modes of inquiry.

DOI

https://doi.org/10.31223/X54W44

Subjects

Hydrology

Keywords

machine learning, stream corridor, inductive

Dates

Published: 2021-05-08 20:07

Last Updated: 2021-09-01 23:45

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License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

Data Availability (Reason not available):
http://www.hydroshare.org/resource/de6d92d314354ea6819157818669fc59

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