Sıcaklık ve güneşlenme süresine dayalı günlük yağış tahmini: Antalya ilinde istasyon bazlı bir modelleme yaklaşımı

Yıl 2025, Cilt: 14 Sayı: 4, 1360 - 1371, 15.10.2025

Hasan Törehan Babacan

https://doi.org/10.28948/ngumuh.1743641

Öz

Sıcaklıkta yaşanan pozitif anomaliler son yıllara iklim değişikliğine bağlı önemli bir araştırma sorusu olmuştur. Sıcaklık değişimleri doğrudan ve dolaylı pek çok etkiye sahiptir. Bu çalışma günlük maksimum sıcaklık ve günlük toplam güneşlenme süresi parametrelerinin günlük yağış üzerindeki etkisini araştırmayı amaçlamaktadır. Bu doğrultuda çalışmada Granger nedensellik analizi yapılarak meteorolojik parametrelerin yağış üzerindeki etkisi araştırılmıştır. Güneşlenme süresi ve günlük maksimum sıcaklık değişkenleri için p istatistik değeri <0.05 olarak belirlenmiş olup iki değişkenin de yağış üzerinde etkin olduğu görülmüştür. Çalışmada parametrelerde çeşitli gün gecikmeleri kullanılarak optimum girdi modelleri oluşturulmuş, Yapay Sinir Ağları (YSA) ve Sınıflandırma ve Regresyon Ağacı (SRA) yöntemleri kullanılarak ilişki düzeyleri araştırılmıştır. Çalışma sonucunda SRA yöntemi ile geliştirilen tahmin modellerinin YSA modellerine göre üstün başarısı (NSE:0.65-0.79) görülmüştür.

Anahtar Kelimeler

Yağış Tahmini , Makine Öğrenmesi , Granger Nedensellik Analizi , Güneşlenme Süresi

Daily rainfall prediction based on temperature and sunshine duration: A station-based modelling approach in Antalya province

Öz

Positive anomalies in temperature have been an important research question related to climate change in recent years. Temperature changes have many direct and indirect effects. This study aims to investigate the effect of daily maximum temperature and total daily sunlight duration parameters on daily rainfall. In this perspective, Granger causality analysis was conducted to investigate the effect of meteorological parameters on rainfall. The p statistic value of the sunshine duration and daily maximum temperature variables was determined as <0.05 and both variables were found to be effective on rainfall. In the study, optimum input models were created using various day lags for the parameters and the relationship levels were investigated using Artificial Neural Networks (ANN) and Classification and Regression Tree (CART) methods. As a result of the study, the prediction models developed with the CART method outperformed the ANN models (NSE: 0.65-0.79).

Anahtar Kelimeler

Rainfall Prediction , Machine Learning , Granger Casuality Test , Sunshine Duration

Kaynakça

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