Comparison of Regression Algorithms to Predict Average Air Temperature

Authors : Berke Oğulcan PARLAK, Hüseyin Ayhan YAVAŞOĞLU

Pages : 312-322

Doi:10.29137/umagd.1232020

View : 59 | Download : 11

Publication Date : 2023-01-31

Article Type : Research Paper

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 insert ignore into journalissuearticles values(RF);, and support vector insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(PRs);, respectively.
Keywords : Hava sıcaklığı tahmini, doğrusal, doğrusal olmayan, regresyon

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