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Synopsis
The main objective of this book and its case study is to present artificial neural networks (ANNs) model using National Oceanic and Atmospheric Administration (NOAA) satellite data to estimate maximum daily air temperature. The proposed model applied in Lake Urmia basin, Iran. The book will take us step by step on how ANNs were used as a new method. In addition to the introduction and the amount of literature review provided, a model with suitable architecture and efficient learning with enough collected data was developed to evaluate the relationships between temperature and parameters influencing the temperature. In brief, air temperature measured in standard climatology stations is one of main descriptive of the environment status. This parameter is of great importance nearby land surface since it regulates many earth processes such as photosynthesis and evapotranspiration. The measured air temperature in climatology stations - which indicates air temperature in vicinity of the station – cannot be used in further distances. Hence, estimation of the maximum air temperature based on satellite data can be a new possibility to determine temperature of an area.
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About the Author
Mohammad Ebrahim Banihabib is a PhD of Water Resource Engineering, Civil Engineering from Kyushu University, Japan and an Associate Professor of University of Tehran, Iran. He has focused on three major research topics: experimental and numerical research on debris flow and its control, hydroinformatics, and strategic planning of water resources.
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Artificial Neural Network Model of Maximum Temperature Using NOAA data
Published by
LAP LAMBERT Academic Publishing Okt 2014, 2014
ISBN 10: 3659618748
ISBN 13: 9783659618741
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main objective of this book and its case study is to present artificial neural networks (ANNs) model using National Oceanic and Atmospheric Administration (NOAA) satellite data to estimate maximum daily air temperature. The proposed model applied in Lake Urmia basin, Iran. The book will take us step by step on how ANNs were used as a new method. In addition to the introduction and the amount of literature review provided, a model with suitable architecture and efficient learning with enough collected data was developed to evaluate the relationships between temperature and parameters influencing the temperature. In brief, air temperature measured in standard climatology stations is one of main descriptive of the environment status. This parameter is of great importance nearby land surface since it regulates many earth processes such as photosynthesis and evapotranspiration. The measured air temperature in climatology stations - which indicates air temperature in vicinity of the station cannot be used in further distances. Hence, estimation of the maximum air temperature based on satellite data can be a new possibility to determine temperature of an area. 92 pp. Englisch. Seller Inventory # 9783659618741
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Artificial Neural Network Model of Maximum Temperature Using NOAA data
Published by
LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659618748
ISBN 13: 9783659618741
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Banihabib Mohammad EbrahimMohammad Ebrahim Banihabib is a PhD of Water Resource Engineering, Civil Engineering from Kyushu University, Japan and an Associate Professor of University of Tehran, Iran. He has focused on three major rese. Seller Inventory # 5168771
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Artificial Neural Network Model of Maximum Temperature Using NOAA data
Published by
LAP LAMBERT Academic Publishing Okt 2014, 2014
ISBN 10: 3659618748
ISBN 13: 9783659618741
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The main objective of this book and its case study is to present artificial neural networks (ANNs) model using National Oceanic and Atmospheric Administration (NOAA) satellite data to estimate maximum daily air temperature. The proposed model applied in Lake Urmia basin, Iran. The book will take us step by step on how ANNs were used as a new method. In addition to the introduction and the amount of literature review provided, a model with suitable architecture and efficient learning with enough collected data was developed to evaluate the relationships between temperature and parameters influencing the temperature. In brief, air temperature measured in standard climatology stations is one of main descriptive of the environment status. This parameter is of great importance nearby land surface since it regulates many earth processes such as photosynthesis and evapotranspiration. The measured air temperature in climatology stations - which indicates air temperature in vicinity of the station - cannot be used in further distances. Hence, estimation of the maximum air temperature based on satellite data can be a new possibility to determine temperature of an area.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 92 pp. Englisch. Seller Inventory # 9783659618741
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Artificial Neural Network Model of Maximum Temperature Using NOAA data
Published by
LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659618748
ISBN 13: 9783659618741
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Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main objective of this book and its case study is to present artificial neural networks (ANNs) model using National Oceanic and Atmospheric Administration (NOAA) satellite data to estimate maximum daily air temperature. The proposed model applied in Lake Urmia basin, Iran. The book will take us step by step on how ANNs were used as a new method. In addition to the introduction and the amount of literature review provided, a model with suitable architecture and efficient learning with enough collected data was developed to evaluate the relationships between temperature and parameters influencing the temperature. In brief, air temperature measured in standard climatology stations is one of main descriptive of the environment status. This parameter is of great importance nearby land surface since it regulates many earth processes such as photosynthesis and evapotranspiration. The measured air temperature in climatology stations - which indicates air temperature in vicinity of the station cannot be used in further distances. Hence, estimation of the maximum air temperature based on satellite data can be a new possibility to determine temperature of an area. Seller Inventory # 9783659618741
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Artificial Neural Network Model of Maximum Temperature Using NOAA data
Published by
LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659618748
ISBN 13: 9783659618741
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Taschenbuch. Condition: Neu. Artificial Neural Network Model of Maximum Temperature Using NOAA data | Mohammad Ebrahim Banihabib (u. a.) | Taschenbuch | 92 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659618741 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 105010622
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Artificial Neural Network Model of Maximum Temperature Using NOAA data
Published by
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ISBN 10: 3659618748
ISBN 13: 9783659618741
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paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Seller Inventory # ERICA82936596187486