Date
2017-08-09Metadata
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Dissertation
Application and Analysis of physical and data-driven stochastic hydrological Simulation-Optimization Methods for the optimal Management of Surface-Groundwater Resources Systems: Iranian Cases Studies
Abstract
Application of physical and data-driven stochastic hydrological simulation-optimization techniques is a major title in water resources planning for the prediction of future water-affecting events and conditions. Appropriate models and algorithms such as conceptual surface-groundwater models, artificial intelligence models (ANN), swarm intelligence and fuzzy logic have been developed and used in many research projects of conjunctive management of surface-groundwater resources in recent years. The use of these models and algorithms leads to an increased accuracy in the modeling of water resources allocation problems, as will be shown in the present thesis by applying various physical and data-driven stochastic hydrological simulation-optimization methods to the optimal management of surface-groundwater resources systems to several Iranian regions which are increasingly being subjected to water stresses in recent decades, not to the least due to ongoing climate change in this part of the world. The combination of these methodologies/case studies has led to 8 individual research publications, either published or in press at the time of this writing.
Citation
@phdthesis{urn:nbn:de:hebis:34-2017080953236,
author={Zare, Mohammad},
title={Application and Analysis of physical and data-driven stochastic hydrological Simulation-Optimization Methods for the optimal Management of Surface-Groundwater Resources Systems: Iranian Cases Studies},
school={Kassel, Universität Kassel, Fachbereich Bauingenieur- und Umweltingenieurwesen},
month={08},
year={2017}
}
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3000 Zare, Mohammad
4000 Application and Analysis of physical and data-driven stochastic hydrological Simulation-Optimization Methods for the optimal Management of Surface-Groundwater Resources Systems: Iranian Cases Studies / Zare, Mohammad
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4060 Online-Ressource
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5550 {{Hydrologie}}
5550 {{Wasserwirtschaft}}
5550 {{Prozessoptimierung}}
5550 {{Simulation}}
7136 ##0##urn:nbn:de:hebis:34-2017080953236
<resource xsi:schemaLocation="http://datacite.org/schema/kernel-2.2 http://schema.datacite.org/meta/kernel-2.2/metadata.xsd"> 2017-08-09T10:32:55Z 2017-08-09T10:32:55Z 2017-08-09 urn:nbn:de:hebis:34-2017080953236 http://hdl.handle.net/123456789/2017080953236 eng Urheberrechtlich geschützt https://rightsstatements.org/page/InC/1.0/ Hydrology Water Resources Management Optimization Simulation Data-driven methods 620 Application and Analysis of physical and data-driven stochastic hydrological Simulation-Optimization Methods for the optimal Management of Surface-Groundwater Resources Systems: Iranian Cases Studies Dissertation Application of physical and data-driven stochastic hydrological simulation-optimization techniques is a major title in water resources planning for the prediction of future water-affecting events and conditions. Appropriate models and algorithms such as conceptual surface-groundwater models, artificial intelligence models (ANN), swarm intelligence and fuzzy logic have been developed and used in many research projects of conjunctive management of surface-groundwater resources in recent years. The use of these models and algorithms leads to an increased accuracy in the modeling of water resources allocation problems, as will be shown in the present thesis by applying various physical and data-driven stochastic hydrological simulation-optimization methods to the optimal management of surface-groundwater resources systems to several Iranian regions which are increasingly being subjected to water stresses in recent decades, not to the least due to ongoing climate change in this part of the world. The combination of these methodologies/case studies has led to 8 individual research publications, either published or in press at the time of this writing. open access Zare, Mohammad Kassel, Universität Kassel, Fachbereich Bauingenieur- und Umweltingenieurwesen Koch, Manfred (Prof. Dr. rer. nat.) Weltzien, Cornelia (Prof. Dr. Ing.) Hydrologie Wasserwirtschaft Prozessoptimierung Simulation 2017-07-21 </resource>
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