Predicting the daily evaporation in Ramadi City by using artificial neural network
Other Title(s)
التنبؤ بالتبخر اليومي باستخدام الشبكات العصبية الصناعية
Author
Source
Anbar Journal for Engineering Sciences
Issue
Vol. 7, Issue 2 (31 Dec. 2017), pp.134-139, 6 p.
Publisher
University of Anbar College of Engineering
Publication Date
2017-12-31
Country of Publication
Iraq
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Abstract EN
In this paper the artificial neural network used to predict dilly evaporation.
The model was trained in MATLAB with five inputs.
The inputs are Min.
Temperature, Max.
Temperature, average temperature, wind speed and humidity.
The data collected from Alramadi meteorological station for one year.
The transfer function models are sigmoid and tangent sigmoid in hidden and output layer, it is the most commonly used nonlinear activation function.
The best numbers of neurons used in this paper was three nodes.
The results concludes, that the artificial neural network is a good technique for predicting daily evaporation, the empirical equation can be used to compute daily evaporation (Eq.6) with regression more than 96% for all (training, validation and testing) as well as, in this model that the Max.
Temperature is a most influence factor in evaporation with importance ratio equal to (30%) then humidity (26%).
American Psychological Association (APA)
al-Mawla, Athir Salim. 2017. Predicting the daily evaporation in Ramadi City by using artificial neural network. Anbar Journal for Engineering Sciences،Vol. 7, no. 2, pp.134-139.
https://search.emarefa.net/detail/BIM-937248
Modern Language Association (MLA)
al-Mawla, Athir Salim. Predicting the daily evaporation in Ramadi City by using artificial neural network. Anbar Journal for Engineering Sciences Vol. 7, no. 2 (2017), pp.134-139.
https://search.emarefa.net/detail/BIM-937248
American Medical Association (AMA)
al-Mawla, Athir Salim. Predicting the daily evaporation in Ramadi City by using artificial neural network. Anbar Journal for Engineering Sciences. 2017. Vol. 7, no. 2, pp.134-139.
https://search.emarefa.net/detail/BIM-937248
Data Type
Journal Articles
Language
English
Notes
Record ID
BIM-937248