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Comparison of artificial neural networks and empirical equations to estimate daily pan evaporation
Author(s) -
Terzi Özlem,
Keskin M. Erol
Publication year - 2010
Publication title -
irrigation and drainage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 38
eISSN - 1531-0361
pISSN - 1531-0353
DOI - 10.1002/ird.454
Subject(s) - evaporation , relative humidity , wind speed , air temperature , constant (computer programming) , artificial neural network , meteorology , correlation coefficient , environmental science , humidity , mean squared error , approximation error , mathematics , statistics , computer science , geography , artificial intelligence , programming language
Abstract This study consists of two parts. In the first part, daily pan evaporation estimations are achieved by a suitable artificial neural network (ANN) model for the meteorological data recorded from the automated GroWheather meteorological station near Lake Eğirdir, which lies in the Lake District of western Turkey. At this station six meteorological variables are measured simultaneously, namely, air temperature, water temperature, solar radiation, air pressure, wind speed and relative humidity. The ANN architecture has only one output neuron with up to four input neurons representing air and water temperatures, air pressure and solar radiation. Prior to ANN model construction the classical correlation study indicated the insignificance of wind speed and relative humidity in the Eğirdir Lake area. Hence, the final ANN model has three input neurons in the input layer with one at the output layer. The hidden layer neuron number is found to be six after various trial and error model runs. In the second part, daily evaporation values are estimated using classical approaches such as the Priestley–Taylor, Brutsaert–Stricker, Makkink and Hamon methods. The comparison was first made using the original constant values involved in each equation, and then using the calibrated constant values. The results show that when the original constant values were used, the Priestley–Taylor, Brutsaert–Stricker and Makkink methods underestimated evaporation values, but the Hamon method overestimated them. When calibrated constant values were substituted for the original constant values, all four equations improved to estimate evaporation. While the mean square error (MSE) values varied between 6.27 and 49.2 for original constant values, they varied between 3.43 and 4.33 for calibrated constant values. Of the evaporation methods, the Hamon method improved well to estimate evaporation values. It is also noted that the ANN model is superior even to the classical approaches of the Priestley–Taylor, Brutsaert–Stricker, Makkink and Hamon methods. Copyright © 2008 John Wiley & Sons, Ltd.