Các bài viết có thể truy cập công khai - Meghna Babbar-SebensTìm hiểu thêm
Không có ở bất kỳ nơi nào: 8
Usability evaluation of an interactive decision support system for user-guided design of scenarios of watershed conservation practices
AD Piemonti, KL Macuga, M Babbar-Sebens
Journal of Hydroinformatics 19 (5), 701-718, 2017
Các cơ quan ủy nhiệm: US National Science Foundation
Trustworthy fairness metric applied to AI-based decisions in food-energy-water
S Uslu, D Kaur, SJ Rivera, A Durresi, M Durresi, M Babbar-Sebens
International Conference on Advanced Information Networking and Applications …, 2022
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Agriculture
Promise of UAV-assisted adaptive management of water resources systems
DJ Hill, M Babbar-Sebens
Journal of Water Resources Planning and Management 145 (7), 02519001, 2019
Các cơ quan ủy nhiệm: Natural Sciences and Engineering Research Council of Canada
Fuzzy and deep learning approaches for user modeling in wetland design
A Hoblitzell, M Babbar-Sebens, S Mukhopadhyay
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2016
Các cơ quan ủy nhiệm: US National Science Foundation, US National Oceanic and Atmospheric …
Uncertainty-based deep learning networks for limited data wetland user models
A Hoblitzell, M Babbar-Sebens, S Mukhopadhyay
2018 IEEE International Conference on Artificial Intelligence and Virtual …, 2018
Các cơ quan ủy nhiệm: US National Science Foundation, US National Oceanic and Atmospheric …
Design and Operation of Agricultural Water Distribution Systems as Hard and Soft Climate Change Adaptation Strategy
M Qiu, M Babbar-Sebens, A Ostfeld
World Environmental and Water Resources Congress 2021, 1069-1080, 2021
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Agriculture
Smart One Water: An Integrated Approach for the Next Generation of Sustainable and Resilient Water Systems
SK Sinha, M Babbar-Sebens, D Dzombak, P Gardoni, B Watford, ...
Oxford Research Encyclopedia of Environmental Science, 2023
Các cơ quan ủy nhiệm: US National Science Foundation
Non-Stationary Reinforcement-Learning Based Dimensionality Reduction for Multi-objective Optimization of Wetland Design
A Hoblitzell, M Babbar-Sebens, S Mukhopadhyay
Proceedings of the 5th International Conference on Robotics and Artificial …, 2019
Các cơ quan ủy nhiệm: US National Science Foundation, US National Oceanic and Atmospheric …
Có tại một số nơi: 21
Effective modeling for Integrated Water Resource Management: A guide to contextual practices by phases and steps and future opportunities
J Badham, S Elsawah, JHA Guillaume, SH Hamilton, RJ Hunt, ...
Environmental Modelling & Software 116, 40-56, 2019
Các cơ quan ủy nhiệm: US National Science Foundation, National Institute for Health Research, UK
Using climate change scenarios to evaluate future effectiveness of potential wetlands in mitigating high flows in a Midwestern US watershed
KM Walters, M Babbar-Sebens
Ecological Engineering 89, 80-102, 2016
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Energy
A trustworthy human–machine framework for collective decision making in food–energy–water management: The role of trust sensitivity
S Uslu, D Kaur, SJ Rivera, A Durresi, M Babbar-Sebens, JH Tilt
Knowledge-Based Systems 213, 106683, 2021
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Agriculture
Trust-based game-theoretical decision making for food-energy-water management
S Uslu, D Kaur, SJ Rivera, A Durresi, M Babbar-Sebens
Advances on Broad-Band Wireless Computing, Communication and Applications …, 2020
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Agriculture
Decision support system using trust planning among food-energy-water actors
S Uslu, D Kaur, SJ Rivera, A Durresi, M Babbar-Sebens
Advanced Information Networking and Applications: Proceedings of the 33rd …, 2020
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Agriculture
Trust-based decision making for food-energy-water actors
S Uslu, D Kaur, SJ Rivera, A Durresi, M Babbar-Sebens
Advanced Information Networking and Applications: Proceedings of the 34th …, 2020
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Agriculture
Interactive genetic algorithm for user‐centered design of distributed conservation practices in a watershed: An examination of user preferences in objective space and user behavior
AD Piemonti, M Babbar‐Sebens, S Mukhopadhyay, A Kleinberg
Water Resources Research 53 (5), 4303-4326, 2017
Các cơ quan ủy nhiệm: US National Science Foundation
Control theoretical modeling of trust-based decision making in food-energy-water management
S Uslu, D Kaur, SJ Rivera, A Durresi, M Babbar-Sebens, JH Tilt
Complex, Intelligent and Software Intensive Systems: Proceedings of the 14th …, 2021
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Agriculture
Emerging themes and future directions of multi-sector nexus research and implementation
Z Khan, E Abraham, S Aggarwal, M Ahmad Khan, R Arguello, ...
Frontiers in Environmental Science 10, 918085, 2022
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Energy
Trustworthy acceptance: a new metric for trustworthy artificial intelligence used in decision making in food–energy–water sectors
S Uslu, D Kaur, SJ Rivera, A Durresi, M Durresi, M Babbar-Sebens
International Conference on Advanced Information Networking and Applications …, 2021
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Agriculture
Water sector infrastructure systems resilience: A social–ecological–technical system-of-systems and whole-life approach
SK Sinha, C Davis, P Gardoni, M Babbar-Sebens, M Stuhr, D Huston, ...
Cambridge Prisms: Water 1, e4, 2023
Các cơ quan ủy nhiệm: US National Science Foundation
Demystifying the fears and myths: The co-production of a regional food, energy, water (FEW) nexus conceptual model
JH Tilt, HA Mondo, NA Giles, S Rivera, M Babbar-Sebens
Environmental Science & Policy 132, 69-82, 2022
Các cơ quan ủy nhiệm: US National Science Foundation, US Department of Agriculture
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