Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt.

ABSTRACT: Air temperature plays a critical role in estimating agricultural water requirements, hydrological processes, and the climate change impacts. This study aims to identify the most accurate forecasting model for daily minimum (T_min) and maximum (T_max) temperatures in a semi-arid environment. Five machine learning models-linear regression (LR), additive regression (AR), support vector machine (SVM), random subspace (RSS), and M5 pruned (M5P)-were compared for T_max and T_min forecasting in Gharbia Governorate, Egypt, using data from 1979 to 2014. The dataset was divided into 75% for training and 25% for testing. Model input combinations were selected based on best subset regression analysis, result shows the best combination was T_min(t-1), T

SUBMITTER: Elbeltagi A 

PROVIDER: S-EPMC12181251 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature