| Surrogate optimization of deep neural networks for groundwater predictions J Müller, J Park, R Sahu, C Varadharajan, B Arora, B Faybishenko, ... Journal of Global Optimization 81, 203-231, 2021 | 94 | 2021 |
| Long-term missing value imputation for time series data using deep neural networks J Park, J Müller, B Arora, B Faybishenko, G Pastorello, C Varadharajan, ... Neural Computing and Applications 35 (12), 9071-9091, 2023 | 49 | 2023 |
| Impact of input feature selection on groundwater level prediction from a multi-layer perceptron neural network RK Sahu, J Müller, J Park, C Varadharajan, B Arora, B Faybishenko, ... Frontiers in Water 2, 573034, 2020 | 39 | 2020 |
| A new multivariate EWMA control chart via multiple testing J Park, CH Jun Journal of Process Control 26, 51-55, 2015 | 33 | 2015 |
| A multistage distributionally robust optimization approach to water allocation under climate uncertainty J Park, G Bayraksan European Journal of Operational Research 306 (2), 849-871, 2023 | 28 | 2023 |
| An exponentially weighted moving average chart controlling false discovery rate SH Lee, JH Park, CH Jun Journal of Statistical Computation and Simulation 84 (8), 1830-1840, 2014 | 25 | 2014 |
| Multivariate process control chart for controlling the false discovery rate JH Park, CH Jun Industrial Engineering and Management Systems 11 (4), 385-389, 2012 | 16 | 2012 |
| Analysis of backwash settings to maximize net water production in an engineering-scale ultrafiltration system for water reuse MA Alhussaini, ZM Binger, BM Souza-Chaves, OO Amusat, J Park, ... Journal of Water Process Engineering 53, 103761, 2023 | 8 | 2023 |
| Variance reduction for sequential sampling in stochastic programming J Park, R Stockbridge, G Bayraksan Annals of Operations Research, 2021 | 5 | 2021 |
| Data-Model Integration and Machine Learning Approaches for Hydrobiogeochemical Modeling Applications C Varadharajan, D Agarwal, B Arora, M Burrus, D Christianson, ... AGU Fall Meeting Abstracts 2021, B15J-1551, 2021 | | 2021 |
| Sensitivity Analysis of Input Feature Selection in Multi-Layer Perceptron Neural Network to Predict Groundwater Levels R Sahu, J Müller, J Park, C Varadharajan, B Arora, B Faybishenko, ... AGU Fall Meeting 2020, 2020 | | 2020 |
| Sensitivity Analysis of Input Feature Selection in Multi-Layer Perceptron Neural Network to Predict Groundwater Levels C Varadharajan, R Sahu, J Müller, J Park, B Arora, B Faybishenko, ... AGU Fall Meeting Abstracts 2020, H166-0032, 2020 | | 2020 |
| A data-driven approach to predicting the impacts of streamflow disturbance on water quality in river corridors C Varadharajan, VC Hendrix, DS Christianson, H Weierbach, J Müller, ... AGU Fall Meeting Abstracts 2020, H103-10, 2020 | | 2020 |
| Fuzzy Rule-Based Systems for Multivariate and Univariate Hydrological Forecasting B Faybishenko, J Mueller, J Mueller, R Sahu, R Sahu, J Park, J Park, ... Geological Society of America Abstracts 52, 358604, 2020 | | 2020 |
| Predicting Daily Groundwater Levels with Deep Learning Models R Sahu, J Müller, J Park, C Varadharajan, B Arora, B Faybishenko, ... AGU Fall Meeting Abstracts 2019, H31I-1839, 2019 | | 2019 |
| Utilizing Diverse Data in Scientific Analysis and Modeling for Water Resource Management C Varadharajan, B Arora, S Cholia, DS Christianson, J Damerow, ... AGU Fall Meeting Abstracts 2019, IN51A-01, 2019 | | 2019 |
| Data-Driven Stochastic Optimization with Application to Water Resources Management J Park The Ohio State University, 2019 | | 2019 |
Juliane MuellerNational Renewable Energy LabVerified email at nrel.gov