Classification of potential evapotranspiration using Self Organizing Map of neural network

Document Type : Original Article

Authors

1 . Assistant professor, Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab

2 Assistant professor in Department of Rang and Watershed Management. Faculty of Agricultural Science. Fasa University, Iran.

Abstract

Potential evapotranspiration has an important role in agricultural studies, the project of water resources management, design of drainage and irrigation network and water structure. According to the importance of this parameters, in this study it has been tried to Classify the potential evapotranspiration using Self Organizing Map (SOM) of neural network in south of Fars province. Parameters that used in this study to classify with SOM method include: minimum temperature, maximum temperature, mean temperature, Sunshine hour, maximum of Sunshine hour, wind speed and relative humidity. Result of this paper shows that minimum temperature, maximum temperature and mean temperature have significant relative in study area. Also the minimum temperature, maximum temperature and mean temperature have invers relation by relative humidity in study area. According to Self Organizing Map (SOM) of neural network method and input parameters of this model, potential evapotranspiration in study area classified in 3 classes. So that class 3 has the highest evapotranspiration and class 1 has the lowest evapotranspiration.

Keywords

References

 
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