DOAJ Open Access 2023

Comparing Regression Techniques for Temperature Downscaling in Different Climate Classifications

Ali Ilghami Kkhosroshahi Mohammad Bejani Hadi Pourali Arman Hosseinpour Salehi

Abstrak

This study aims to identify the optimal regression techniques for downscaling among ten commonly used methods in climatology, including SVR, LinearSVR, LASSO, LASSOCV, Elastic Net, Bayesian Ridge, RandomForestRegressor, AdaBoost Regressor, KNeighbors Regressor, and XGBRegressor. For the Köppen climate classification system, including A (tropical), B (dry), C (temperate), and D (continental), synoptic station data were collected. Furthermore, for the purpose of downscaling, a general circulation model (GCM) had been utilized. Additionally, to enhance the performance of downscaling accuracy, mutual information (MI) was employed for feature selection. The downscaling performance was evaluated using the coefficient of determination (DC) and root mean square error (RMSE). Results indicate that SVR had superior performance in tropical and dry climates and LassoCV with RandomForestRegressor had better results in temperate and continental climates.

Topik & Kata Kunci

Engineering machinery, tools, and implements

Penulis (4)

Ali Ilghami Kkhosroshahi

Mohammad Bejani

Hadi Pourali

Arman Hosseinpour Salehi

Format Sitasi

APA MLA BibTeX

Kkhosroshahi, A.I., Bejani, M., Pourali, H., Salehi, A.H. (2023). Comparing Regression Techniques for Temperature Downscaling in Different Climate Classifications. https://doi.org/10.3390/ASEC2023-15256

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber doi.org/10.3390/ASEC2023-15256

Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.3390/ASEC2023-15256
Akses: Open Access ✓