Beschreibung
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.
Autorenporträt
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.
Herstellerkennzeichnung:
OmniScriptum SRL
Str. Armeneasca 28/1, office 1
2012 Chisinau
MD
E-Mail: info@omniscriptum.com
