TY  - JOUR
TI  - Estimation of Cloudiness Data Based on Multiple Linear Regression Model
AB  - 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.
AU  - Zateroglu, Mine Tulin
DO  - 10.31466/kfbd.1150879
PY  - 2023
JO  - Karadeniz Fen Bilimleri Dergisi
VL  - 13
IS  - 1
SN  - 2564-7377
SP  - 33
EP  - 41
DB  - TRDizin
UR  - http://search/yayin/detay/1161821
ER  -