Berke Oğulcan PARLAK, Hüseyin Ayhan YAVAŞOĞLU

Ortalama Hava Sıcaklığını Tahmin Etmek İçin Regresyon Algoritmalarının Karşılaştırılması

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.

Anahtar Kelimeler:

Hava sıcaklığı tahmini, doğrusal, doğrusal olmayan, regresyon

Comparison of Regression Algorithms to Predict Average Air Temperature

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,

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Abdel-Aal, R. E. (2004). Hourly temperature forecasting using abductive networks. Engineering Applications of Artificial Intelligence, 17(5), 543–556.
Al-Obeidat, F., Spencer, B., & Alfandi, O. (2020). Consistently accurate forecasts of temperature within buildings from sensor data using ridge and lasso regression. Future Generation Computer Systems, 110, 382–392.
Alaruri, S. D., & Amer, M. F. (1993). Empirical regression models for weather data measured in Kuwait during the years 1985, 1986, and 1987. Solar Energy, 50(3), 229–233.
Avdakovic, S., Ademovic, A., & Nuhanovic, A. (2013). Correlation between air temperature and electricitydemand by linear regression and wavelet coherence approach: UK, Slovakia and Bosnia and Herzegovina case study. Archives of Electrical Engineering, 62(4).
Bahrami, M., & Mahmoudi, M. R. (2022). Long-term temporal trend analysis of climatic parameters using polynomial regression analysis over the Fasa Plain, southern Iran. Meteorology and Atmospheric Physics, 134(2), 1–12.
Bastien, P., Vinzi, V. E., & Tenenhaus, M. (2005). PLS generalised linear regression. Computational Statistics & Data Analysis, 48(1), 17–46.
Benyahya, L., Caissie, D., St-Hilaire, A., Ouarda, T. B. M. J., & Bobée, B. (2007). A review of statistical water temperature models. Canadian Water Resources Journal, 32(3), 179–192.
Chevalier, R. F. (2008). Air temperature prediction using support vector regression and GENIE: The Georgia Extreme-weather Neural-network Informed Expert. University of Georgia.
Duan, S., Yang, W., Wang, X., Mao, S., & Zhang, Y. (2019). Grain pile temperature forecasting from weather factors: A support vector regression approach. 2019 IEEE/CIC International Conference on Communications in China (ICCC), 255–260.
He, Y., Chen, C., Li, B., & Zhang, Z. (2022). Prediction of near-surface air temperature in glacier regions using ERA5 data and the random forest regression method. Remote Sensing Applications: Society and Environment, 28, 100824.
Holmstrom, M., Liu, D., & Vo, C. (2016). Machine learning applied to weather forecasting. Meteorol. Appl, 10, 1–5.
Houthuys, L., Karevan, Z., & Suykens, J. A. K. (2017). Multi-view LS-SVM regression for black-box temperature prediction in weather forecasting. 2017 International Joint Conference on Neural Networks (IJCNN), 1102–1108.
Jakaria, A. H. M., Hossain, M. M., & Rahman, M. A. (2020). Smart weather forecasting using machine learning: a case study in tennessee. ArXiv Preprint ArXiv:2008.10789.
Karna, N., Roy, P. C., & Shakya, S. (2018). Temperature Prediction using Regression Model.
Lan, Y., & Zhan, Q. (2017). How do urban buildings impact summer air temperature? The effects of building configurations in space and time. Building and Environment, 125, 88–98.
Massaron, L., & Boschetti, A. (2016). Regression analysis with Python. Packt Publishing Ltd.
Paniagua-Tineo, A., Salcedo-Sanz, S., Casanova-Mateo, C., Ortiz-García, E. G., Cony, M. A., & Hernández-Martín, E. (2011). Prediction of daily maximum temperature using a support vector regression algorithm. Renewable Energy, 36(11), 3054–3060.
Riordan, D., & Hansen, B. K. (2002). A fuzzy case-based system for weather prediction. Engineering Intelligent Systems for Electrical Engineering and Communications, 10(3), 139–146.
Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with python. Proceedings of the 9th Python in Science Conference, 57(61), 10–25080.
Shafin, A. A. (2019). Machine learning approach to forecast average weather temperature of Bangladesh. Global Journal of Computer Science and Technology, 19(3), 39–48.
Stančin, I., & Jović, A. (2019). An overview and comparison of free Python libraries for data mining and big data analysis. 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 977–982.
Verikas, A., Vaiciukynas, E., Gelzinis, A., Parker, J., & Olsson, M. C. (2016). Electromyographic patterns during golf swing: Activation sequence profiling and prediction of shot effectiveness. Sensors, 16(4), 592.
Vicente-Serrano, S. M., Saz-Sánchez, M. A., & Cuadrat, J. M. (2003). Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature. Climate Research, 24(2), 161–180.
Zhang, Q., Cheng, J., & Wang, N. (2021). Fusion of All-Weather Land Surface Temperature From AMSR-E and MODIS Data Using Random Forest Regression. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.