GENETİK EVRİMSEL PROGRAMLAMA İLE YAĞIŞ TAHMİN MODELİ

Year 2015, Volume: 7 Issue: 1, 8 - 21, 01.03.2015

Emine Dilek Taylan

Abstract

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.

Keywords

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

PRECIPITATION PREDICTION MODEL WITH GENETIC EVALUATIONARY PROGRAMMING

Abstract

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

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