Emine Dilek TAYLAN

7023

GENETİK EVRİMSEL PROGRAMLAMA İLE YAĞIŞ TAHMİN MODELİ

Bu çalışmanın amacı Genetik Evrimsel Programlama (GEP) ve Yapay Sinir Ağları (YSA) yöntemlerini kullanarak en uygun yağış tahmin modelini geliştirmektir. Söz konusu metotlar Türkiye’de Göller Bölgesinde yeralan Eğirdir’e düşen yağışı tahmin etmek için kullanılmışlardır. Eğirdir’e ait yağış verileri aynı bölgede yeralan Isparta ve Senirkent istasyomlarının yağış verileri kullanılarak tahmin edilmiştir. Aylık yağış tahminleri için veriler Meteoroloji Genel Müdürlüğü’nden alınmıştır. Kullanılan meteorolojik veriler 1975 yılından 2010 yılına kadar olan 36 yıllık periyottan oluşmaktadır. GEP ve YSA modelleri için farklı girdi değişkenleri denenerek en uygun girdi seti elde edilmeye çalışılmıştır. Model sonuçları ile tarihi yağış kayıtları mukayese edildiğinde GEP modellerinin YSA modellere göre daha iyi sonuçlar verdiği görülmüştür. GEP ile geliştirilen yağış modeli sayesinde eksik ya da ölçülmemiş yağış verilerinin tahmini aynı zamanda en düşük ve en yüksek yağış verilerinin tahmini kolaylıkla yapılabilecektir.
Anahtar Kelimeler:

Aylık Yağış, Genetik Evrimsel Programlama, Yapay Sinir Ağları, Eğirdir

PRECIPITATION PREDICTION MODEL WITH GENETIC EVALUATIONARY PROGRAMMING

The aim of this study was to develop an optimum precipitation prediction model, based on genetic evaluationary programming (GEP) and artificial neural network (ANN). The methodologies were applied to predict precipitation in Eğirdir located in the Lakes District of Turkey. The precipitation values of Eğirdir station were predicted using precipitation values of Isparta and Senirkent stations located in same region. For monthly precipitaion predictions, the data were taken from Turkish State Meteorological Service. The used data covered 36 years period during 1975-2010 for monthly precipitations. The GEP and ANN models were developed using different combinations of input variables. The comparison of historical records and models showed a better agreement in the GEP models than ANN models. With the help of GEP model for integrated precipitaton prediction, it is possible to estimate missing or unmeasured data and it was good at prediction of min and max precipitations.
Keywords:

Monthly precipitation, Genetic Evaluationary Programming, Artificial Neural Networks, Eğirdir,

PDF

___

  • Ab. Ghani,, A., and Md. Azamathulla, H., 2011.Gene-Expression Programming for Sediment Transport in Sewer Pipe Systems., J. Pipeline Syst. Eng. Pract. 2(3), 102–106.
  • Ahmad,S., Simonovic,S. P., 2005. An artificial neural network model for generating hydrograph from hydro-meteorological parameters. Journal of Hydrology. 315, 236-251.
  • Braddock,R.D., Kremmer,M.L., Sanzogni,L., 1998. Feed-forward artificial neural network model for forecasting rainfall run-off. Environmetrics.9, 419-432.
  • Chen, S.H., Lin, Y.H., Chang, L.C., Chang, F.J., 2006. The strategy of building a flood forecast model by neuro-fuzzy network. Hydrol. Processes. 20, 1525-1540.
  • Dibike,Y.B., Solomatine, D.P., 2001. Stream flow forecasting using artificial neural networks. Phys. Chem. Earth (B). 26, 1-7.
  • Ferreira, C., 2001. Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst. 13(2),87–129
  • Ghorbani, M.A., Khatibi, R., Aytek, A., Makarynskyy, O., Shiri, J. 2010. Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks.,Comput Geosci. 36,620–627
  • Güven, A., Aytek, A., 2009. New approach for stage–discharge relationship: gene-expression programming. J Hydrol Eng. 14(8), 812–820.
  • Hashmi, M.Z., Shamseldin, A.Y., Melville, B.W., 2011. Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP).Environmental Modelling and Software. 26(12), 1639-1646.
  • Imrie,C.E., Durucan,S., Korre,A., 2000. Stream flow prediction using artificial neural networks: generalisation beyond the calibration range. Journal of Hydrology.233, 138- 153.
  • Jervase,J.A., Al-Lawati, A., Dorvlo,A.S.S., 2002. Contour maps for sunshine ratio for Oman using radial basis function generated data. Renewable Energy.28, 487-497.
  • Keskin,M.E., Terzi, Ö., 2006. Artificial Neural Network models of Daily pan Evaporation. Journal of Hydrologic Engineering.11(1), 65-70.
  • Lin,C.T., Lee,C.S.G., 1996. Neural Fuzzy Systems. Prentice Hall P.T.R., Upper Saddle Stream, NJ 07458.
  • Luk,K.C., Ball,J.E., Sharma,A., 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. Journal of Hydrology.227, 56-65.
  • McCulloch,W.S., Pitts,W., 1943. A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115-133.
  • Ramirez,M. C. V., Velho,H. F. C., Ferreira,N. J., 2005. Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. Journal of Hydrology. 301, 146- 162.
  • Reddy, M.J., Ghimire, B.N.S., 2009. Use of model tree and gene expression programming to predict the suspended sediment load in rivers. J Intell Syst. 18(3),211–228.
  • Rodríguez, V.K., Arganis, J., M.L,Cruickshank, V.C., Domínguez, M.R., 2012. Rainfall- runoff modelling using genetic programming.Journal of Hydroinformatics.14(1),108-121.
  • Taylan, D., Küçükyaman, D., 2011. Artificial Neural Networks for Precipitation Prediction: A Case Study on Eğirdir. INISTA 2011, International Symposium on Innovations in Intelligent Systems and Applications. 15-18 June. Istanbul , Turkey.
  • Teegavarapu Ramesh, S.V., Chandramouli,V., 2005. Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. Journal of Hydrology. 312, 191–206.
  • Teegavarapu, R.S.V., Tufail, M., Ormsbee, L., 2009. Optimal functional forms for estimation of missing precipitation data. J Hydrol. 374,106–115.
  • Tingsanchali,T., Gautam, M. R., 2000. Application of tank, NAM, ARMA and neural network models to flood forecasting. Hydrol. Process.14, 2473-2487.
  • Whigham, P.A., Crapper, P.F., 2001. Modelling rainfall-runoff using genetic programming. Math Comput Model. 33,707–721
  • Zahiri, A., Azamathulla, H M., 2012. Comparison between linear genetic programming and M5 tree models to predict flow discharge in compound channels. Neural Comput & Applic. DOI 10.10 07/s00521-012-1247-0.
  • Zealand,C.M., Burn,D.H., Simonovic,S.P., 1999. Short term streamflow forecasting using artificial neural networks. Journal of Hydrology.214, 32-48.
Uluslararası Teknolojik Bilimler Dergisi-Cover
  • Başlangıç: 2009
  • Yayıncı: Isparta Uygulamalı Bilimler Üniversitesi
Arşiv
Sayıdaki Diğer Makaleler

DSP TEMELLİ DA-DA DÜŞÜRÜCÜ ÇEVİRİCİ TASARIMI VE UYGULAMASI

Murat ULUĞ, Okan BİNGÖL

HÜCRESEL SİNİR AĞLARI VE DALGACIK DÖNÜŞÜMÜ KULLANARAK FLOK VE FİLAMENTLERİN BÖLÜTLENMESİ

Halime BOZTOPRAK

SÜPER AKIŞKANLAŞTIRICI KATKILARIN AĞIR BETONLARIN RADYASYON GEÇİRGENLİĞİNE ETKİSİ

Şemsettin KILINÇARSLAN, Gülce ŞENLİ

GENETİK EVRİMSEL PROGRAMLAMA İLE YAĞIŞ TAHMİN MODELİ

Emine Dilek TAYLAN

KARADENİZ VE SAKARYA HAVZALARINDA YILLIK ORTALAMA YAĞIŞLARIN TREND ANALİZİ

Gokmen ÇERİBAŞI, Emrah DOĞAN