Bulutluluk Verisinin Çoklu Doğrusal Regresyon Modeli Kullanılarak Tahmin Edilmesi

Year 2023, Volume: 13 Issue: 1, 33 - 41, 15.03.2023

Mine Tulin Zateroglu

https://doi.org/10.31466/kfbd.1150879

Cited By: 1

Abstract

Bu çalışma, iklim verileri ve hava kalite indisi gibi meteorolojik değişkenler kullanılarak bulutla kaplılık verisini tahmin etmektedir. Bu çalışmada kullanılan tüm meteorolojik parametrelerin günlük ortalama gözlem değerleri 1990-2015 dönemi için aylık ortalama verilere dönüştürülmüştür. Aylık ortalama bulutluluk değerleri, Kayseri'de kentsel alanda diğer iklim unsurları ve değer hava kalitesi indeksi kullanılarak tahmin edilmiştir. Bulutluluğu tahmin etmek için matematiksel ilişkileri belirlemek için Çoklu Doğrusal Regresyon modeli oluşturulmuştur. Meteorolojik parametrelerin bulutluluğu en fazla Mayıs ve Ekim aylarında, en az ise Eylül ve Ocak aylarında etkilediği gösterilmiştir. Ayrıca tahmin edilen modellere göre hava kalitesi indeks değeri Ocak, Temmuz, Ekim ve Kasım aylarındaki bulutluluk verileri üzerinde istatistiksel olarak anlamlı etkiye sahiptir.

Keywords

Bulutluluk, İklimsel Değişkenler, Hava Kalitesi İndis Değeri, Çoklu Doğrusal Regresyon Modeli

Estimation of Cloudiness Data Based on Multiple Linear Regression Model

Abstract

This study estimates cloudiness data using meteorological parameters which include climatic variables and air quality index. Daily average observed values of all meteorological parameters used in this study were transformed to monthly mean data for 1990-2015 period. The monthly mean values of cloudiness were estimated by using the other climatic elements and the value air quality index at urban area in Kayseri. Multiple Linear Regression model was built to determine the mathematical relationships for predicting cloudiness. It has been shown that meteorological parameters affect cloudiness the most in May and October, and the least in September and January. Additionally, according to the estimated models, air quality index value has effect on cloudiness data on January, July, October and November as statistically significant.

Keywords

Cloudiness, Climatic Variables, Air Quality Index Value, Multiple Linear Regression Model

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