Jeffrey Sadler

Assistant Professor

Oklahoma State University

United States

Research Interests

Explore related searches

Professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Jeffrey Sadler is an Assistant Professor at Oklahoma State University in the United States. His research primarily focuses on the application of machine learning in hydrology and environmental modeling, with recent articles exploring stream salinization, dissolved oxygen prediction, and the impact of climate change on stream discharge. He is dedicated to enhancing the understanding of river water quality and has contributed to advancements in stormwater management and environmental modeling.

Articles (13)

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Collaborators (3)

David G. Tarboton

Utah State University

UNITED STATES

View details

Mohammed Ombadi

Assistant Professor

University of Michigan

UNITED STATES

View details

Hong Yi

University of North Carolina at Chapel Hill

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Jeffrey Sadler

Research Interests

Explore related searches

Contact this professor

About

Articles (13)

Evaluating a process‐guided deep learning approach for predicting dissolved oxygen in streams

Quantifying the Impact of Climate Change on Peak Stream Discharge for Watersheds of Varying Sizes in the Coastal Plain of Virginia

Predictive Understanding of Stream Salinization in a Developed Watershed Using Machine Learning

Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms

Multi‐Task Deep Learning of Daily Streamflow and Water Temperature

Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?

Impact of Geospatial Data Enhancements for Regional-Scale 2D Hydrodynamic Flood Modeling: Case Study for the Coastal Plain of Virginia

Toward open and reproducible environmental modeling by integrating online data repositories, computational environments, and model Application Programming Interfaces

Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community

A taxonomy for reproducible and replicable research in environmental modelling

Exploring real-time control of stormwater systems for mitigating flood risk due to sea level rise

Collaborators (3)

David G. Tarboton

Mohammed Ombadi

Hong Yi

Make your next move.