Comparison of artificial neural networks and empirical equations to estimate daily pan evaporation

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 Egirdir, 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 Egirdir 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. RÉ SUMÉCette étude se compose de deux parties. Dans la première partie, des évaluations quotidiennes d'évaporation sont réalisées par un modèle approprié de réseau neuronal (ANN) pour les données météorologiques enregistrées à partir de la station météorologique automatisée de GroWheather près du lac Egirdir qui se situe dans la Région des lacs de la Turquie occidentale. A cette station six variables météorologiques sont mesurées simultanément, à savoir, température de l'air, température de l'eau, rayonnement solaire, pression atmosphérique, vitesse de vent et humidité relative. L'architecture de l'ANN a seulement un neurone en sortie avec jusqu'à quatre neurones en entrée représentant les températures de l'eau et de l'air, la pression atmosphérique et la radiation solaire. Avant la

Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree

“…The artificial intelligence methods has been successfully applied for modeling pan evaporation (Ep) in the last decades (Bruton et al, 2000;Dogan et al, 2007;Goyal et al, 2014;Karimi-Googhari, 2010;Kim and Kim, 2008;Kim et al, 2014;Kisi, 2005Kisi, , 2006Kisi, , 2009aKisi, ,b, 2013Lin et al, 2013;Malik and Kumar, 2015;Moghaddamnia et al, 2010;Nourani and Sayyah Fard, 2012;Piri et al, 2009a;Samui and Dixon, 2012;Sanikhani et al, 2012;Sudheer et al, 2002;Terzi and Keskin, 2008;Yang, 2013). Bruton et al (2000) developed artificial neural network (ANN) models for estimating daily Ep.…”

Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis, Greece

“…Recently, a significant number of articles have used ANNs to simulate evaporation from lakes/reservoirs and evapotranspiration. Terzi and Keskin (2010) compared the ANN and four conventional methods to estimate daily pan evaporation and suggested that the conventional methods should be calibrated before use with the ANN method to give better results. Diamantopoulou et al (2011) tested two ANN models of the back-propagation algorithm and the cascade correlation architecture to estimate daily reference evapotranspiration with minimum meteorological data.…”

Meteorological drought analysis using artificial neural networks

“…Given a training set of input-output pairs, the algorithm provides a procedure for changing the weights in a backpropagation network to classify the given input patterns correctly. The basis for this weight update algorithm is simply the gradient-descent method as used for simple perceptrons with differentiable neurons (Terzi and Keskin, 2010).…”