Jeffrey M. Sadler
Jeffrey M. Sadler
Oklahoma St. University
Verified email at - Homepage
Cited by
Cited by
Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest
JM Sadler, JL Goodall, MM Morsy, K Spencer
Journal of hydrology 559, 43-55, 2018
Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks
BD Bowes, JM Sadler, MM Morsy, M Behl, JL Goodall
Water 11 (5), 1098, 2019
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
FT Zahura, JL Goodall, JM Sadler, Y Shen, MM Morsy, M Behl
Water Resources Research 56 (10), e2019WR027038, 2020
Physics-guided recurrent graph model for predicting flow and temperature in river networks
X Jia, J Zwart, J Sadler, A Appling, S Oliver, S Markstrom, J Willard, S Xu, ...
Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021
Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5
JM Sadler, JL Goodall, M Behl, MM Morsy, TB Culver, BD Bowes
Environmental Modelling & Software 120, 104484, 2019
Exploring real-time control of stormwater systems for mitigating flood risk due to sea level rise
JM Sadler, JL Goodall, M Behl, BD Bowes, MM Morsy
Journal of Hydrology 583, 124571, 2020
Design of a metadata framework for environmental models with an example hydrologic application in HydroShare
MM Morsy, JL Goodall, AM Castronova, P Dash, V Merwade, JM Sadler, ...
Environmental Modelling & Software 93, 13-28, 2017
A cloud-based flood warning system for forecasting impacts to transportation infrastructure systems
MM Morsy, JL Goodall, GL O'Neil, JM Sadler, D Voce, G Hassan, ...
Environmental modelling & software 107, 231-244, 2018
Impact of sea-level rise on roadway flooding in the Hampton Roads region, Virginia
JM Sadler, N Haselden, K Mellon, A Hackel, V Son, J Mayfield, A Blase, ...
Journal of Infrastructure Systems 23 (4), 05017006, 2017
Integrating scientific cyberinfrastructures to improve reproducibility in computational hydrology: Example for HydroShare and GeoTrust
BT Essawy, JL Goodall, W Zell, D Voce, MM Morsy, J Sadler, Z Yuan, ...
Environmental Modelling & Software 105, 217-229, 2018
Toward open and reproducible environmental modeling by integrating online data repositories, computational environments, and model Application Programming Interfaces
YD Choi, JL Goodall, JM Sadler, AM Castronova, A Bennett, Z Li, ...
Environmental Modelling & Software 135, 104888, 2021
Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?
C Varadharajan, AP Appling, B Arora, DS Christianson, VC Hendrix, ...
Hydrological Processes 36 (4), e14565, 2022
Multi‐task deep learning of daily streamflow and water temperature
JM Sadler, AP Appling, JS Read, SK Oliver, X Jia, JA Zwart, V Kumar
Water Resources Research 58 (4), e2021WR030138, 2022
A taxonomy for reproducible and replicable research in environmental modelling
BT Essawy, JL Goodall, D Voce, MM Morsy, JM Sadler, YD Choi, ...
Environmental Modelling & Software 134, 104753, 2020
A recipe for standards-based data sharing using open source software and low-cost electronics
JM Sadler, DP Ames, R Khattar
Journal of Hydroinformatics 18 (2), 185-197, 2016
Physics-guided recurrent graph networks for predicting flow and temperature in river networks
X Jia, J Zwart, J Sadler, A Appling, S Oliver, S Markstrom, J Willard, S Xu, ...
arXiv preprint arXiv:2009.12575, 2020
Physics-guided machine learning from simulation data: An application in modeling lake and river systems
X Jia, Y Xie, S Li, S Chen, J Zwart, J Sadler, A Appling, S Oliver, J Read
2021 IEEE International Conference on Data Mining (ICDM), 270-279, 2021
Heterogeneous stream-reservoir graph networks with data assimilation
S Chen, A Appling, S Oliver, H Corson-Dosch, J Read, J Sadler, J Zwart, ...
2021 IEEE International Conference on Data Mining (ICDM), 1024-1029, 2021
Partial differential equation driven dynamic graph networks for predicting stream water temperature
T Bao, X Jia, J Zwart, J Sadler, A Appling, S Oliver, TT Johnson
2021 IEEE International Conference on Data Mining (ICDM), 11-20, 2021
Extending HydroShare to enable hydrologic time series data as social media
JM Sadler, DP Ames, SJ Livingston
Journal of Hydroinformatics 18 (2), 198-209, 2016
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