DAILY PRECIPITATION PREDICTION IN ISPARTA STATION BY ARTIFICIAL NEURAL NETWORK

1. DAILY PRECIPITATION PREDICTION IN ISPARTA STATION BY ARTIFICIAL NEURAL NETWORK Kemal SAPLIOĞLU Mesut ÇİMENBilgehan AKMAN kemsa@sdu.edu.tr mesutcim@mmf.sdu.edu.tr Department of Civil Engineering, Suleyman Demirel University, Isparta, Turkey

2. Rainfall information is important for food production plan, water resource management and all activity plans in the nature. The occurrence of prolonged dry period or heavy rain at the critical stages of the crop growth and development may lead to significant reduce crop yield. A wide range of rainfall forecast methods are employed in weather forecasting at regional and national levels. It is possible to collect the models of precipitation forecast under two main headlines as experimental and dynamic. • Experimental approaches the methods generally based on data which has historical basis and has a great amount of regression among them. The most important of them is artificial neural network; stochastic models, fuzzy logic and data based group models [1] • But the dynamic approaches base on equation classes which are composed by banding climatic conditions and chances in atmosphere together.

3. [2] Formed an influential regression technique for the model of Monsoon rain which is long periodical and seen in summer months. While forming this model, he uses snowfall in Asia, temperature degrees in Northwest Europe, pressure zones occurring in Europe and Asia, surface temperatures occurring in Arabian sea and temperature values occurring a year before in Indian Ocean. Consequently; he formed his model with % 4 error lot. [3] stablished a multiple linear regression model. In this model, he used sea level, the temperature of sea surface and Southern Elnino resonance index. As an offspring of the model, he detects % 60 correlations. • Also, another way commonly used in our country and by many researchers is the completion of lack data via the arithmetic mean method. In this method the formulation, shown in equation 1, is being used [4]. •   (1) • At there, the precipitation recorded in the period which is calculated P1, 2,3,…n the annual total precipitation amount N1,2,3,…n. • The other formula which can be used for the completion of missing data is harmonic average method which focuses mainly on the distance of the station whose precipitation data will be completed to the other stations. • In this study, new models are formed with artificial neural network and changeable neuron numbers to be able to predict the lack data. This model formed with available arithmetic mean methods.

4. Eğirdir Station: 37050’15’’ North, and 30052’19’’ East coordinates; the altitude 920 m; the distance to Isparta station D=27.78 km. Isparta Station: 37047’05’’ North, and 30034’04’’ East coordinates; the altitude 920 m. Burdur Station: 37042’57’’ North, and 30019’13’’ East coordinates; the altitude 987 m; the distance to Isparta station D=21.94 km.

6. Burdur 1 neuron 2 neuron Isparta Eğirdir 3 neuron

7. Training datanumber=8100 January1-1975 and December31-2007 Data number=11689 Testing datanumber=3589

10. Table 1. The Comparison of Training and Testing Models Obtained From ANN models and Weighted and Harmonic Average procedures

11. YSA Model Results

12. Weighted Mean Method

13. Harmonic MeanMethod

14. CONCLUSION • In this study, the ANN models with different neuron number are designed to complete the missing data of the stations which has no measurement during the precipitation or to complete the missing data of newly opened station. • These models are compared with the weighted and harmonic average methods. As a result of calculations it is understood that the ANN model with 5 neurons which gives the smallest error values and which creates appropriate scatter diagram, is superior then the other two calculation methods. • For this study region, it is observed that the weighted average method forecasts the daily precipitation of the station where the study is carrying out better than harmonic average method. • Moreover, it is observed that the weighted and harmonic average methods are useful in the stations which have similar hydrometeorology and are close to each other. • Consequently, it can be said that the ANN models are superior than the methods examined in this study.

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