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  2. Volume 7, Issue 2
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ISSN: 1997-9428

Volume7, Issue2

Predicting the Daily Evaporation in Ramadi City by Using Artificial Neural Network

    Atheer Saleem Almawla

Anbar Journal for Engineering Sciences, 2017, Volume 7, Issue 2, Pages 134-139

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Abstract

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%).
Keywords:
    daily evaporation model Artificial Neural Network Predicting
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(2017). Predicting the Daily Evaporation in Ramadi City by Using Artificial Neural Network. Anbar Journal for Engineering Sciences, 7(2), 134-139.
Atheer Saleem Almawla. "Predicting the Daily Evaporation in Ramadi City by Using Artificial Neural Network". Anbar Journal for Engineering Sciences, 7, 2, 2017, 134-139.
(2017). 'Predicting the Daily Evaporation in Ramadi City by Using Artificial Neural Network', Anbar Journal for Engineering Sciences, 7(2), pp. 134-139.
Predicting the Daily Evaporation in Ramadi City by Using Artificial Neural Network. Anbar Journal for Engineering Sciences, 2017; 7(2): 134-139.
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Journal Information

Publisher: University of Anbar

Email:  ajes.uoanbar.edu.iq

Editor-in-chief: Prof. Dr. Yousif Al Mashhadany

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