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  • Online Resource  (6)
  • 2020-2024  (6)
  • Modellierung  (6)
  • 1
    Online Resource
    Online Resource
    Deep learning for hydrometeorology and environmental science (2021)
    Cham, Switzerland : Springer
    Details
    UID:
    b3kat_BV047135703
    Format: 1 Online-Ressource (xiv, 204 Seiten) , Illustrationen
    ISBN: 9783030647773
    Series Statement: Water science and technology library volume 99
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-64776-6
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-64778-0
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-64779-7
    Language: English
    Keywords: Hydrologie ; Einzugsgebiet ; Modellierung ; Wasserwirtschaft ; Governance
    DOI: 10.1007/978-3-030-64777-3
    URL: Volltext  (URL des Erstveröffentlichers)
    Author information: Singh, Vijay P. 1946-
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    FU Berlin
    BTU Cottbus
    TU Berlin
  • 2
    Online Resource
    Online Resource
    Hydrological Modeling : Hydraulics, Water Resources and Coastal Engineering (2022)
    Cham : Springer International Publishing | Cham : Springer
    Details
    UID:
    b3kat_BV047875416
    Format: 1 Online-Ressource (X, 526 Seiten)
    ISBN: 9783030813581
    Series Statement: Water Science and Technology Library 109
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-81357-4
    Language: English
    Subjects: Geography
    RVK:
    RB 10351
    Keywords: Hydrologie ; Modellierung ; Küstenschutz ; Wasserversorgung ; Aufsatzsammlung
    DOI: 10.1007/978-3-030-81358-1
    URL: Volltext  (URL des Erstveröffentlichers)
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    Online Access
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    FU Berlin
    BTU Cottbus
    TU Berlin
  • 3
    Online Resource
    Online Resource
    Water management and water governance : hydrological modeling (2021)
    Cham, Switzerland : Springer
    Details
    UID:
    b3kat_BV047047675
    Format: 1 Online-Ressource (xi, 550 Seiten) , Illustrationen, Diagramme
    ISBN: 9783030580513
    Series Statement: Water science and technology library volume 96
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-58050-6
    Language: English
    Keywords: Hydrologie ; Einzugsgebiet ; Modellierung ; Wasserwirtschaft ; Governance
    DOI: 10.1007/978-3-030-58051-3
    URL: Volltext  (URL des Erstveröffentlichers)
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    FU Berlin
    BTU Cottbus
  • 4
    Online Resource
    Online Resource
    Hydrological Extremes : River Hydraulics and Irrigation Water Management (2021)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    UID:
    gbv_1741575079
    Format: 1 Online-Ressource(XII, 446 p. 262 illus., 230 illus. in color.)
    Edition: 1st ed. 2021.
    ISBN: 9783030591489
    Series Statement: Water Science and Technology Library 97
    Content: Intelligent Irrigation Water Management -- River Hydraulics -- Assessment and Monitoring of Hydrological Extremes -- Future Scenarios.
    Content: This book presents quality technical papers representing the recent developments in the field of hydrological modeling, water management and water governance including practical applications. The content covers multifarious aspects of hydrology and water resources. It includes an application of the Hydrologic Modelling System (HEC-HMS) which has been successfully demonstrated for assessment of floods. The authors suggest an approach for the mitigation of cyclone disaster through a case study of the Phailin cyclone, whilst considering mitigating pluvial flooding, developing suitable management strategies. The book includes chapters discussing the detrended fluctuation analysis which is carried out for multifractal description of droughts. Drought characteristics are analyzed, and drought indices evolved for drought preparedness/management. The use of science in community planning under changing climate is also studied and discussed. The authors present and experimental study wherein hydraulic coefficients are calibrated by using vertical orifice. A cross flow hybrid hydrokinetic turbine is also evaluated for performance, and high head regulating radial gate designed and studied its sensitivity. This book will appeal to researchers, field practitioners, NGO and other Governmental as well as private water practitioners .
    Additional Edition: ISBN 9783030591472
    Additional Edition: ISBN 9783030591496
    Additional Edition: ISBN 9783030591502
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030591472
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030591496
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030591502
    Language: English
    Keywords: Hydrometeorologie ; Hydrologie ; Wasserhaushalt ; Wasserkreislauf ; Starkregen ; Überschwemmung ; Sri Lanka ; Überflutung ; Dürre ; Hochwasser ; Trockenheit ; Prognose ; Indien ; Bewässerungsplanung ; Landwirtschaft ; Bewässerung ; Abflussregime ; Flussbett ; Wasserströmung ; Modellierung
    DOI: 10.1007/978-3-030-59148-9
    URL: lizenzpflichtig
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    UB Potsdam
  • 5
    Online Resource
    Online Resource
    Water Management and Water Governance : Hydrological Modeling (2021)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    UID:
    gbv_1741575060
    Format: 1 Online-Ressource(XI, 550 p. 265 illus., 231 illus. in color.)
    Edition: 1st ed. 2021.
    ISBN: 9783030580513
    Series Statement: Water Science and Technology Library 96
    Content: 1. Application of RS and GIS in Watershed Management -- 2. Application of RS and GIS in Agricultural Water Management -- 3. Hydrological Modeling -- 4. Intelligent Water Management -- 5. Water Governance.
    Content: This book focusses on hydrological modeling, water management, and water governance. It covers the applications of remote sensing and GIS tools and techniques for land use and land cover classifications, estimation of precipitation, evaluation of morphological changes, and monitoring of soil moisture variability. Moreover, remote sensing and GIS techniques have been applied for crop mapping to assess cropping patterns, computation of reference crop evapotranspiration, and crop coefficient. Hydrological modeling studies have been carried out to address various issues in the water sector. MODFLOW model was successfully applied for groundwater modeling and groundwater recharge estimation. Runoff modeling has been carried out to simulate the snowmelt runoff together with the rainfall and sub-surface flow contributions for snow-fed basins. A study has been included, which predicts the impact of the land use and land cover on stream flow. Various problems in the water sector have been addressed employing hydrological models such as SWAT, ArcSWAT, and VIC. An experimental study has been presented wherein the laboratory performance of rainfall simulator has been evaluated. Hydrological modeling studies involving modifications in the curve number methodology for simulation of floods and sediment load have also been presented. This book is useful for academicians, water practitioners, scientists, water managers, environmentalists, and administrators, NGOs, researchers, and students who are involved in water management with the focus on hydrological modeling, water management, and water governance.. .
    Additional Edition: ISBN 9783030580506
    Additional Edition: ISBN 9783030580520
    Additional Edition: ISBN 9783030580537
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030580506
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030580520
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030580537
    Language: English
    Keywords: Indien ; Flusssystem ; Einzugsgebiet ; Abflussregime ; Grundwasser ; Indien ; Hydrologie ; Modellierung ; Fernerkundung
    DOI: 10.1007/978-3-030-58051-3
    URL: lizenzpflichtig
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    UB Potsdam
  • 6
    Online Resource
    Online Resource
    Deep Learning for Hydrometeorology and Environmental Science (2021)
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Details
    UID:
    gbv_1746360992
    Format: 1 Online-Ressource(XIV, 204 p. 189 illus., 133 illus. in color.)
    Edition: 1st ed. 2021.
    ISBN: 9783030647773
    Series Statement: Water Science and Technology Library 99
    Content: Introduction -- Mathematical Background -- Data Preprocessing -- Neural Network -- Training a Neural Network -- Updating Weights -- Improving model performance -- Advanced Neural Network Algorithms -- Deep learning for time series -- Deep learning for spatial datasets -- Tensorflow and Keras Programming for Deep Learning -- Hydrometeorological Applications of deep learning -- Environmental Applications of deep learning.
    Content: This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
    Additional Edition: ISBN 9783030647766
    Additional Edition: ISBN 9783030647780
    Additional Edition: ISBN 9783030647797
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030647766
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030647780
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030647797
    Language: English
    Keywords: Hydrometeorologie ; Hydrologie ; Maschinelles Lernen ; Angewandte Mathematik ; Wasserhaushalt ; Prognose ; Neuronales Netz ; Modellierung ; Mathematisches Modell
    DOI: 10.1007/978-3-030-64777-3
    URL: lizenzpflichtig
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    UB Potsdam
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