Year: 2025
Type: article
Abstract: Traditional statistical methods are difficult to meet the needs of the demands of temperature prediction. To improve the predicted performance of Boosting algorithm, this study adopts an improved Random Forest algorithm for the feature selection of meteorological data and employs Random Search and Bayesian Optimization for the parameter optimization of the model. The experimental result indicates that the Gradient Boosting Decision Tree(GBDT) based on Bayesian Optimization demonstrates significant superiority in temperature prediction.
Source: Theoretical and Natural Science
Author: Yumeng Ban
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410128787