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Patrick Reed
Patrick Reed
Joseph C. Ford Professor of Engineering, School of Civil and Environmental Engineering, Cornell
Bestätigte E-Mail-Adresse bei cornell.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Borg: An auto-adaptive many-objective evolutionary computing framework
D Hadka, P Reed
Evolutionary computation 21 (2), 231-259, 2013
7252013
State of the art for genetic algorithms and beyond in water resources planning and management
J Nicklow, P Reed, D Savic, T Dessalegne, L Harrell, A Chan-Hilton, ...
Journal of Water Resources Planning and Management 136 (4), 412-432, 2010
6962010
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
HR Maier, Z Kapelan, J Kasprzyk, J Kollat, LS Matott, MC Cunha, ...
Environmental Modelling & Software 62, 271-299, 2014
6042014
Evolutionary multiobjective optimization in water resources: The past, present, and future
PM Reed, D Hadka, JD Herman, JR Kasprzyk, JB Kollat
Advances in water resources 51, 438-456, 2013
5872013
Many objective robust decision making for complex environmental systems undergoing change
JR Kasprzyk, S Nataraj, PM Reed, RJ Lempert
Environmental Modelling & Software 42, 55-71, 2013
5202013
Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation
Y Tang, P Reed, T Wagener, K Van Werkhoven
Hydrology and Earth System Sciences 11 (2), 793-817, 2007
3782007
Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design
JB Kollat, PM Reed
Advances in Water Resources 29 (6), 792-807, 2006
3672006
How should robustness be defined for water systems planning under change?
JD Herman, PM Reed, HB Zeff, GW Characklis
Journal of Water Resources Planning and Management 141 (10), 04015012, 2015
3562015
The future of water resources systems analysis: Toward a scientific framework for sustainable water management
CM Brown, JR Lund, X Cai, PM Reed, EA Zagona, A Ostfeld, J Hall, ...
Water resources research 51 (8), 6110-6124, 2015
3052015
Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization
D Hadka, P Reed
Evolutionary computation 20 (3), 423-452, 2012
2692012
Designing a competent simple genetic algorithm for search and optimization
P Reed, B Minsker, DE Goldberg
Water Resources Research 36 (12), 3757-3761, 2000
2432000
The food‐energy‐water nexus: Transforming science for society
BR Scanlon, BL Ruddell, PM Reed, RI Hook, C Zheng, VC Tidwell, ...
Water Resources Research 53 (5), 3550-3556, 2017
2382017
Cost‐effective long‐term groundwater monitoring design using a genetic algorithm and global mass interpolation
P Reed, B Minsker, AJ Valocchi
Water Resources Research 36 (12), 3731-3741, 2000
2342000
Characterization of watershed model behavior across a hydroclimatic gradient
K van Werkhoven, T Wagener, P Reed, Y Tang
Water Resources Research 44 (1), 2008
2322008
Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models
K Van Werkhoven, T Wagener, P Reed, Y Tang
Advances in Water Resources 32 (8), 1154-1169, 2009
2252009
A framework for visually interactive decision-making and design using evolutionary multi-objective optimization (VIDEO)
JB Kollat, P Reed
Environmental Modelling & Software 22 (12), 1691-1704, 2007
2222007
Beyond optimality: Multistakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty
JD Herman, HB Zeff, PM Reed, GW Characklis
Water Resources Research 50 (10), 7692-7713, 2014
2182014
Striking the balance: long-term groundwater monitoring design for conflicting objectives
PM Reed, BS Minsker
Journal of Water Resources Planning and Management 130 (2), 140-149, 2004
2132004
Curses, tradeoffs, and scalable management: Advancing evolutionary multiobjective direct policy search to improve water reservoir operations
M Giuliani, A Castelletti, F Pianosi, E Mason, PM Reed
Journal of Water Resources Planning and Management 142 (2), 04015050, 2016
2122016
Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models
JD Herman, JB Kollat, PM Reed, T Wagener
Hydrology and Earth System Sciences 17 (7), 2893-2903, 2013
1992013
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