Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality

Charuleka Varadharajan; Vipin Kumar; Jared Willard; Jacob Aaron Zwart; Jeffrey Michael Sadler; Helen Weierbach; Talita Perciano; Juliane Mueller; Valerie Hendrix; Danielle Christianson

doi:10.2172/1769795

USGS · 70248718

Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality

Charuleka Varadharajan·Vipin Kumar·Jared Willard·Jacob Aaron Zwart·Jeffrey Michael Sadler·Helen Weierbach·Talita Perciano·Juliane Mueller·Valerie Hendrix·Danielle Christianson

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Abstract

This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.

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Charuleka Varadharajan, Vipin Kumar, Jared Willard, Jacob Aaron Zwart, Jeffrey Michael Sadler, Helen Weierbach, Talita Perciano, Juliane Mueller, Valerie Hendrix, Danielle Christianson. 2021. Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality. https://doi.org/10.2172/1769795

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