Comparison of Regression Algorithms to Predict Average Air Temperature

Year 2023, Volume: 15 Issue: 1, 312 - 322, 31.01.2023

Berke Oğulcan Parlak Hüseyin Ayhan Yavaşoğlu

https://doi.org/10.29137/umagd.1232020

Abstract

Regression algorithms are statistical techniques used to predict the value of a dependent variable, based on one or more independent variables. These algorithms are commonly used in fields such as economics, finance, and engineering. Temperature prediction is a specific application of regression analysis. In this case, the dependent variable is temperature and the independent variables include factors such as humidity, speed of the wind, direction of the wind, and precipitation. There are many different types of regression algorithms, each with its strengths and weaknesses. The study compares the performance of multiple regression models in predicting the average air temperature, using one month's weather data for the Beşiktaş district of Istanbul. A total of 6 different regression models, including ridge, lasso, linear, polynomial, random forest (RF), and support vector (SV) regressions, were included in the study. Among the regression models trained and tested on two different data sets, the three most successful models in predicting average air temperature were lasso, RF, and polynomial regressions (PRs), respectively.

Keywords

Air temperature forecast, linear, nonlinear, regression

Ortalama Hava Sıcaklığını Tahmin Etmek İçin Regresyon Algoritmalarının Karşılaştırılması

Abstract

Regresyon algoritmaları, bir veya daha fazla bağımsız değişkene dayalı olarak bağımlı bir değişkenin değerini tahmin etmek için kullanılan istatistiksel tekniklerdir. Bu algoritmalar ekonomi, finans ve mühendislik gibi alanlarda yaygın olarak kullanılmaktadır. Sıcaklık tahmini, regresyon analizinin özel bir uygulamasıdır. Bu durumda bağımlı değişken sıcaklıktır ve bağımsız değişkenler nem, rüzgar hızı, rüzgar yönü ve yağış gibi faktörleri içerir. Güçlü ve zayıf yönleri olan birçok farklı regresyon algoritması türü vardır. Çalışma, İstanbul'un Beşiktaş ilçesi için bir aylık hava durumu verilerini kullanarak, ortalama hava sıcaklığını tahmin etmede farklı regresyon modellerinin performansını karşılaştırmaktadır. Çalışmaya ridge, lasso, lineer, polinom, rastgele orman (RO) ve destek vektörü (DV) regresyonları olmak üzere toplam 6 farklı regresyon modeli dahil edilmiştir. İki farklı veri seti üzerinde eğitilen ve test edilen regresyon modelleri arasında, ortalama hava sıcaklığını tahmin etmede en başarılı üç model sırasıyla lasso, RO ve polinom regresyonları (PR) olmuştur.

Keywords

Hava sıcaklığı tahmini, doğrusal, doğrusal olmayan, regresyon

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