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    Abstract
2017 (Vol. 14, Issue: 10)
Research Article

A Committee Machine with Intelligent Systems for Estimating Monthly Mean Reference Evapotranspiration in an Arid Region

1Ali H. Al-Aboodi, 1Alaa M. Al-Abadi and 1Husham T. Ibrahim
1Department of Civil Engineering, College of Engineering, University of Basrah, Basrah, Iraq
2Department of Geology, College of Sciences, University of Basrah, Basrah, Iraq
 

DOI: 10.19026/rjaset.14.5131
Submitted Accepted Published
May 15, 2017 June 23, 2017 October 15, 2017

  How to Cite this Article:

1Ali H. Al-Aboodi, 1Alaa M. Al-Abadi and 1Husham T. Ibrahim, 2017. A Committee Machine with Intelligent Systems for Estimating Monthly Mean Reference Evapotranspiration in an Arid Region.  Research Journal of Applied Sciences, Engineering and Technology, 14(10): 386-398.

DOI: 10.19026/rjaset.14.5131

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=RJASET&doi=rjaset.14.5131

Abstract:


The aim of this research is to estimate the reference evapotranspiration ETo as given by FAO-56 PM equation in Basrah city, southern Iraq by using several climatic inputs data including maximum monthly mean air temperature, minimum monthly mean air temperature, monthly mean relative humidity and monthly mean wind speed. Three artificial intelligent systems (generalized regression neural network GRNN, multi-layer perceptron MLP and adaptive neuro-fuzzy inference systems ANFIS) were used for predicting reference evapotranspiration. Root mean squared error and coefficient of determination were used as comparison criteria for evaluation of performance of all the developed models. The results shown that the models performances of multi-layer perceptron models are better than adaptive neuro-fuzzy inference systems models and slightly better than generalized regression neural network models with different inputs combination. A Committee Machine with Intelligent Systems (CMIS) was constructed for estimation of ETo by integrating the results of predicting ETo from GRNN, MLP and ANFIS, each of them has a weight factor representing its contribution in overall estimation. The results illustrated that the performance of committee machine with intelligent systems is better than any one of the individual artificial intelligent systems for predicting ETo.

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    Competing interests

The authors have no competing interests.
    Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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© The Author(s) 2017

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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