Ibrahim GURER 1 , F.Ebru YILDIZ 2

1. COMPARISON OF THE DIFFERENT EVAPORATION COMPUTATION METHODS (ARTIFICIAL NEURAL NETWORK, PENMAN AND THORNTHWAITE) AT SULTANSAZLIGI WETLAND KAYSERI-TURKEY Ibrahim GURER1,F.Ebru YILDIZ2 1Gazi University, Engineering Faculty, Civil Engineering Dept. Maltepe/Ankara/TURKEY 2Iller Bank, Department of Project Development, Opera/Ulus/Ankara/TURKEY

2. INTRODUCTION Develi Plain is located at the Central Anatolia near the Kayseri City. Sultansazligi Wetland is one of the seven important wetland of Turkey. Sultansazligi is located at the center of Develi, Yeşilhisar and Yahyalı districts. Yay Lake, Cöl Lake, Southern and Northern Marshlands are placed in Sultansazligi Wetland Region.There are three dams within Develi Closed Basin for the irrigation purpose.

3. AIM OF THE STUDY In this STUDY, the comparison of the calculation techniques, in order to determine the evaporation from free water surface of the lakes at Sultansazligi Wetland, are given. Long term monthly evaporation values from free water surface were calculated by using Penman, Thornthwaite and Artificial Neural Network Modeling Methods. These evaporation values were compared with the real pan evaporation measurements.

4. METEOROLOGICAL DATA Meteorological data (air temperature, solar radiation, wind speed and realtive humidity) wereprovided from Turkish State Hydraulics Works (DSI) and Turkish State Meteorological Service (DMI) and then long term monthly evaporation values from free water surface were calculated by using Penman, Thornthwaite and Artificial Neural Network Modeling Methods Meteorology Station operated by DSI at Develi Closed Basin

5. Meteorology and flow measurement stations at Develi Closed Basin

6. REAL EVAPORATION DATA The real evaporation measurements measured by Class A Pan were obtained from DSI, then calculated evaporation values compared with the real evaporation measurements. DSI meteorology station staff measures evaporation at 08:00 AM everyday from Class A evaporation pan. Due to the weather conditions; in order to minimize the observation errors Class A Pan is used only in dry months from April to October. The evaporation measurements measured by Class A Pan has to be decreased by a pan evaporation coefficient. Pan evaporation coefficient had been determined as 0.62 for Sultansazligi Wetland so pan evaporation values were multiplied by 0.62 in this study. Class A evaporation pan measurements of Musahicali and Yay Lake stations at Sultansazligi Wetland, had been obtained from Turkish State Hydraulic Works (DSI)

7. CALCULATION OF EVAPORATION AT SULTANSAZLIGI WETLAND • Thornthwaite Method 2. Penman Method 3. Artificial Neural Network Sultansazlığı Wetland

8. Thornthwaite Method Table : Evaporation at Sultansazligi Wetland by using Thornthwaite Method

9. Penman Method Table : Monthly evaporation at Sultansazligi Wetland by using Penman Method

10. The symbols used in the evaporation calculations can be explained as below: Rc: Actual short wave radiation from the sun and sky at Earth’s surface Rb:Net long wave radiation of the Earth Ra: Angot’s value of solar radiation arriving at the outher limit of the atmosphere in g.cal/cm2/day n: Actual hours of sunshine N: Possible hours of sunshine r: Albedo (reflection coefficient) of the surface (equal to 0.06 for free water surfcaces) σ: Lummer or Pringsheim constant , equal to 117.74*10-9 g.cal/cm2/day Ta: Absolute Earth temperature in Kelvin ( oK ) (oK= oC +273) es: Saturation water pressure of air in mm Hg at temperature oC ea: Actual water pressure of air in mm Hg at temperature oC Rh:Relative humidity U2: average wind speed obtained from Develi Meteorology Station in m/sec

11. Artificial Neural Network An artificial neural network (ANN) is a mathematical model or a computational model that is inspired by the structure and/or functional aspects of biological neural networks. An ANN has three types of parameters as shown below: 1) The interconnection pattern between different layers of neurons 2) The learning process for updating the weights (synaptic weighting factors) of the interconnections 3) The activation function that converts a neuron's weighted input to its output activation Artificial neurons are used as components in larger systems that combine both adaptive and non-adaptive elements

12. Figure: An artificial neural network system (www.learnartificialneuralnetworks.com)

13. The interval activity (summing function) xj is the input value of the j th neuron and wkj is the weighted factor net The input parameters in this study are: air temperature, water temperature and short wave solar radiation, these parameters are designated by x1, x2 and x3 in the equation for the calculation of the summing function. The Activation Function (Sigmoid Function) The output of the neuron, yi is EVAPORATION in this study. The activation function acts as a squashing function, such that the output of a neuron in a neural network is between certain values. Sigmoid function is generally used as the activation function.

14. In the content of this study: a neural network model using back propagation algorithm was developed. A commonly used method is the mean-squared error method for the back propagation method, which tries to minimize the average squared error between the network's output yi and the target value y. The real evaporation values are used as target y values and weighted factors are computed for each neurons by using iteration. Sultansazlığı Wetland There are three layers in this model. These are: input layer, hidden layer and output layer.

15. Steps of Calculation: 1) Calculation of interval activity (net1 )by using Summing function, input parameters and weighted factors for the Hidden Layer. 2) Calculation ofyi= f1(net1)by usingSigmoid function for the Hidden Layer. Calculated yivalues are theinput values of the Output layer. 3) Calculation of interval activitynet2by using Summing function, input parameters and weighted factors.Calculated yivalues are used as input parameters of the Output Layer. 4) Calculation ofyk= f2(net2)by usingSigmoid function for theOutput Layer. Calculated yivalue is the evaporation value from free surface of Sultansazligi Wetland. Output of the hidden layer and also input of the output layer yi = yk is the EVAPORATION FROM FREE WATER SURFACE

16. INPUT AND OUTPUT OF THE ANN (Artificial Neural Network) MODEL *Ta: air temperature (Co), **Tw: water temperature(Co), ***Rc : Short wave solar radiation ****E: evaporation from free water surface calculated by Artificial Neural Network (mm)

17. DISCUSSION OF THE RESULTS Thornthwaite Method gives evaporation values deviating from others, because Thornwaite Method is a method used for calculating potential evapotranspiration and uses less number of meteorological parameters than Artificial Neural Network (ANN) and Penman Methods. On the other hand ANN and Penman Methods give similar results with the real Class A pan evaporation measurements. In addition there is difference with the Artificial Neural Network Model and the real evaporation observations on June. Water temperature measurement errors on May and June may cause this difference between the real evaporation and the estimated evaporation value by using ANN Method.

18. 1998 1999 As can be seen in the Figure there is great difference between water and air temperatures on May-June because of the measurement errors.

19. Distribution graph for the Penman and Artificial Neural Network Methods during April-October months monthly computed evaporation (mm) It can be said both the correlation coefficients (0.9387 and 0.9529) of ANN and Penman Methods are high enough to use these methods for the evaporation estimations.

20. CONCLUSION and RECOMMENDATION Artificial Neural Network (ANN) and Penman Method give similar results when compared with the real Class A pan evaporation measurements. Thornwaite Method is a method calculating potential evapotranspiration so comptuted evaporation values by usingThornwaite Method are different when compared with the real Class A pan evaporation measurements. Automatic meteorology station can be used which is measuring daily water-air temperatures, real humidity, wind speed, solar radiation and evaporation from free water surface in order to get more reliable results. In the content of this study average monthly meteorological data were used because of the lack of the daily data, additionally some missing data were calculated by using regression equations. Daily meteorological data can be used for Artificial Neural Network and Penman Methods for the evaporation estimation and then these daily evaporation values can be compared with the daily real evaporation values.

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