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  • 1
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : MDPI - Multidisciplinary Digital Publishing Institute
    UID:
    gbv_1778494951
    Format: 1 Online-Ressource (376 p.)
    ISBN: 9783038975489
    Content: This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water
    Note: English
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    gbv_1853347299
    Format: 1 Online-Ressource (302 p.)
    ISBN: 9783036577852 , 9783036577845
    Content: The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices. The thirteen research papers published in this Special Issue make significant contributions to long- and short-term hydrological modeling and water resources management under changing environments using AI techniques coupled with various analytics tools. These contributions, which cover hydrological forecasting, microclimate control, and climate adaptation, can promote hydrology research and direct policy making toward sustainable and integrated water resources management
    Note: English
    Language: Undetermined
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Online Resource
    Online Resource
    Basel :MDPI - Multidisciplinary Digital Publishing Institute,
    UID:
    edoccha_9961153282502883
    Format: 1 online resource (302 pages)
    Content: The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices. The thirteen research papers published in this Special Issue make significant contributions to long- and short-term hydrological modeling and water resources management under changing environments using AI techniques coupled with various analytics tools. These contributions, which cover hydrological forecasting, microclimate control, and climate adaptation, can promote hydrology research and direct policy making toward sustainable and integrated water resources management.
    Additional Edition: ISBN 3-0365-7784-X
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    Online Resource
    Online Resource
    Basel :MDPI - Multidisciplinary Digital Publishing Institute,
    UID:
    almahu_9949521657302882
    Format: 1 online resource (302 pages)
    Content: The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices. The thirteen research papers published in this Special Issue make significant contributions to long- and short-term hydrological modeling and water resources management under changing environments using AI techniques coupled with various analytics tools. These contributions, which cover hydrological forecasting, microclimate control, and climate adaptation, can promote hydrology research and direct policy making toward sustainable and integrated water resources management.
    Additional Edition: ISBN 3-0365-7784-X
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Online Resource
    Online Resource
    Basel :MDPI - Multidisciplinary Digital Publishing Institute,
    UID:
    edocfu_9961153282502883
    Format: 1 online resource (302 pages)
    Content: The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles, as well as urban, agricultural, and industrial water cycles, to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices. The thirteen research papers published in this Special Issue make significant contributions to long- and short-term hydrological modeling and water resources management under changing environments using AI techniques coupled with various analytics tools. These contributions, which cover hydrological forecasting, microclimate control, and climate adaptation, can promote hydrology research and direct policy making toward sustainable and integrated water resources management.
    Additional Edition: ISBN 3-0365-7784-X
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    UID:
    b3kat_BV049343285
    Format: 1 Online-Ressource (IX, 289 Seiten)
    ISBN: 9783036577845
    Note: Special issue reprint "Water"
    Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-0365-7785-2
    Language: English
    Subjects: Computer Science , Geography , Agriculture, Forestry, Horticulture, Fishery, Domestic Science , General works
    RVK:
    RVK:
    RVK:
    RVK:
    Keywords: Künstliche Intelligenz ; Hydrologie ; Wasserversorgung ; Wasserwirtschaft ; Ressourcenmanagement ; Aufsatzsammlung
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
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  • 7
    Online Resource
    Online Resource
    Basel ; Beijing ; Wuhan ; Barcelona ; Belgrade : MDPI
    UID:
    b3kat_BV045524128
    Format: 1 Online-Ressource
    ISBN: 9783038975496
    Note: This is a reprint of articles from the special issue published online in the open access journal Water (ISSN 2073-4441) from 2018 to 2019 (available at: https://www.mdpi.com/journal/water/special_issues/flood_forecast).
    Additional Edition: Erscheint auch als Druck-Ausgabe, paperback ISBN 978-3-03897-548-9
    Language: English
    Keywords: Hochwasservorhersage ; Maschinelles Lernen ; Aufsatzsammlung
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    Online Resource
    Online Resource
    MDPI - Multidisciplinary Digital Publishing Institute
    UID:
    almahu_9949711657302882
    Format: 1 electronic resource (376 p.)
    Content: This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water
    Note: English
    Additional Edition: ISBN 3-03897-548-6
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    Online Resource
    Online Resource
    MDPI - Multidisciplinary Digital Publishing Institute
    UID:
    edocfu_9959145883202883
    Format: 1 electronic resource (376 p.)
    Content: This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water
    Note: English
    Additional Edition: ISBN 3-03897-548-6
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 10
    Online Resource
    Online Resource
    MDPI - Multidisciplinary Digital Publishing Institute
    UID:
    edoccha_9959145883202883
    Format: 1 electronic resource (376 p.)
    Content: This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water
    Note: English
    Additional Edition: ISBN 3-03897-548-6
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
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