Milad Hooshyar
Milad Hooshyar
Adresă de e-mail confirmată pe princeton.edu - Pagina de pornire
Citat de
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A game theory–reinforcement learning (GT–RL) method to develop optimal operation policies for multi-operator reservoir systems
K Madani, M Hooshyar
Journal of Hydrology 519, 732-742, 2014
Wet channel network extraction by integrating LiDAR intensity and elevation data
M Hooshyar, S Kim, D Wang, SC Medeiros
Water Resources Research 51 (12), 10029-10046, 2015
Reconstructing annual groundwater storage changes in a large-scale irrigation region using GRACE data and Budyko model
Y Tang, M Hooshyar, T Zhu, C Ringler, AY Sun, D Long, D Wang
Journal of Hydrology 551, 397-406, 2017
Valley and channel networks extraction based on local topographic curvature and k‐means clustering of contours
M Hooshyar, D Wang, S Kim, SC Medeiros, SC Hagen
Water Resources Research 52 (10), 8081-8102, 2016
An analytical solution of Richards' equation providing the physical basis of SCS curve number method and its proportionality relationship
M Hooshyar, D Wang
Water Resources Research 52 (8), 6611-6620, 2016
Hydrologic controls on junction angle of river networks
M Hooshyar, A Singh, D Wang
Water Resources Research 53 (5), 4073-4083, 2017
Quantifying climatic controls on river network branching structure across scales
S Ranjbar, M Hooshyar, A Singh, D Wang
Water Resources Research 54 (10), 7347-7360, 2018
Optical cloud pixel recovery via machine learning
S Tahsin, SC Medeiros, M Hooshyar, A Singh
Remote Sensing 9 (6), 527, 2017
Channelization cascade in landscape evolution
S Bonetti, M Hooshyar, C Camporeale, A Porporato
Proceedings of the National Academy of Sciences 117 (3), 1375-1382, 2020
Linear layout of multiple flow-direction networks for landscape-evolution simulations
SK Anand, M Hooshyar, A Porporato
Environmental Modelling & Software 133, 104804, 2020
Climatological, virological and sociological drivers of current and projected dengue fever outbreak dynamics in Sri Lanka
CE Wagner, M Hooshyar, RE Baker, W Yang, N Arinaminpathy, G Vecchi, ...
Journal of the Royal Society Interface 17 (167), 20200075, 2020
Nash-reinforcement learning (N-RL) for developing coordination strategies in non-transferable utility games
K Madani, M Hooshyar, S Khatami, A Alaeipour, A Moeini
2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2014
Climatic controls on landscape dissection and network structure in the absence of vegetation
M Hooshyar, A Singh, D Wang, E Foufoula‐Georgiou
Geophysical Research Letters 46 (6), 3216-3224, 2019
Variational analysis of landscape elevation and drainage networks
M Hooshyar, S Anand, A Porporato
Proceedings of the Royal Society A 476 (2239), 20190775, 2020
Aggregation–decomposition-based multi-agent reinforcement learning for multi-reservoir operations optimization
M Hooshyar, SJ Mousavi, M Mahootchi, K Ponnambalam
Water 12 (10), 2688, 2020
Interbasin and intrabasin competitions control drainage network density
M Hooshyar, A Singh, D Wang
Geophysical Research Letters 46 (2), 661-669, 2019
From turbulence to landscapes: Logarithmic mean profiles in bounded complex systems
M Hooshyar, S Bonetti, A Singh, E Foufoula-Georgiou, A Porporato
Physical Review E 102 (3), 033107, 2020
Spectral signature of landscape channelization
M Hooshyar, G Katul, A Porporato
Geophysical Research Letters 48 (8), e2020GL091015, 2021
Fluctuation theorem and extended thermodynamics of turbulence
A Porporato, M Hooshyar, AD Bragg, G Katul
Proceedings of the Royal Society A 476 (2243), 20200468, 2020
Comment on “Groundwater Affects the Geomorphic and Hydrologic Properties of Coevolved Landscapes” by Litwin et al.
SK Anand, S Bonetti, C Camporeale, M Hooshyar, A Porporato
Journal of Geophysical Research: Earth Surface 127 (10), e2022JF006669, 2022
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