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 ET_o by integrating the results of predicting ET_o 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 ET_o.

Keywords:

Adaptive neuro-fuzzy inference systems, artificial neural network, basrah, committee machine, evapotranspiration, Iraq,

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