Artificial Neural Network Model of Maximum Temperature Using NOAA data - Couverture souple

Banihabib, Mohammad Ebrahim ; Arabi, Azar ; Salha, Ali A.; Rahimikhoob, Ali

9783659618741: Artificial Neural Network Model of Maximum Temperature Using NOAA data

Couverture souple

ISBN 10 : 3659618748 ISBN 13 : 9783659618741

Editeur : LAP LAMBERT Academic Publishing, 2014

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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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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2014
Langue: anglais
ISBN 10: 3659618748
ISBN 13: 9783659618741
Reliure: Broch�
Nombre de pages: 92
Coordonn�es du fabricant: non disponible
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Artificial Neural Network Model of Maximum Temperature Using NOAA data

Mohammad Ebrahim Banihabib

Edit� par LAP LAMBERT Academic Publishing Okt 2014, 2014

ISBN 10 : 3659618748 ISBN 13 : 9783659618741

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783659618741

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Artificial Neural Network Model of Maximum Temperature Using NOAA data

Mohammad Ebrahim Banihabib|Azar Arabi|Ali A. Salha|Ali Rahimikhoob

Edit� par LAP LAMBERT Academic Publishing, 2014

ISBN 10 : 3659618748 ISBN 13 : 9783659618741

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Etat : 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. N� de r�f. du vendeur 5168771

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Artificial Neural Network Model of Maximum Temperature Using NOAA data

Mohammad Ebrahim Banihabib

Edit� par LAP LAMBERT Academic Publishing Okt 2014, 2014

ISBN 10 : 3659618748 ISBN 13 : 9783659618741

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Taschenbuch. Etat : 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 Landstra�e 99, 66123 Saarbr�cken 92 pp. Englisch. N� de r�f. du vendeur 9783659618741

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Artificial Neural Network Model of Maximum Temperature Using NOAA data

Mohammad Ebrahim Banihabib

Edit� par LAP LAMBERT Academic Publishing, 2014

ISBN 10 : 3659618748 ISBN 13 : 9783659618741

Neuf Taschenbuch

impression � la demande

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783659618741

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