Prediction of Monthly Evaporation Model Using Artificial Intelligent Techniques in the Western Desert of Iraq-Al-Ghadaf Valley

Abstract

The use of traditional methods to predict evaporation may face many obstacles due to the influence of many factors on the pattern of evaporation's shape. Therefore, the use of existing methods of artificial intelligence is a reliable prediction model in many applications in engineering. Monthly measurements were employed in the present work to predict for duration eighteen years, from beginning of January 2000 until December 2017. The best model was chosen using ANNs (MLP, RBF) and AI (SVM) techniques. The best evaporation model prediction was made using ANNs (MLP, RBF) and AI (SVM) technologies, with temperature, wind speed, relative humidity, and sunshine hours used as independent variables. Several statistical metrics have been used to evaluate the effectiveness of the proposed model to other popular artificial intelligence models. The obtained result denotes the superiority of the MLP models over the RBF and SVM models. It is concluded that the MLP model is better than RBF and SVM for evaporation prediction for both groups. A comparison of the model performance between MLP, RBF, and SVM models indicated that the MLP-ANN method presents the best estimates of monthly evaporation rate with minimum RMSE 0.033, minimum MAE 0.026, and maximum determination coefficient 0.967.

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