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

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

Technical Report

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

https://doi.org/10.2172/1769795

Metrics

3

Crossref references

Web analytics dashboard Metrics definitions

Links

More information: Publisher Index Page (via DOI)
Open Access Version: External Repository
Download citation as: RIS | Dublin Core

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.

Additional publication details
Publication type	Report
Publication Subtype	Federal Government Series
Title	Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality
Series title	Technical Report
DOI	10.2172/1769795
Year Published	2021
Language	English
Publisher	Department of Energy
Contributing office(s)	WMA - Integrated Information Dissemination Division
Description	5 p.