Journal Article

Machine Learning-Based Modeling of Air Temperature in the Complex Environment of Yerevan City, Armenia

Tepanosyan, Garegin, Khlghatyan, Anahit, Muradyan, Vahagn, Avetisyan, Rima, Hovsepyan, Azatuhi, Dell’Acqua, Fabio, Ayvazyan, Grigor, Asmaryan, Shushanik

2023

Overview

Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between the predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques.

• Publisher: MDPI AG
• Subject: Air temperature / Armenia / Artificial intelligence / Atmospheric temperature / Climate change / Comparative analysis / Complexity / Environmental aspects / Independent variables / Land surface temperature / landscapes / Learning algorithms / least squares / Least squares method / Machine learning / machine learning (ML) / Measurement / multiple independent variables / Parameter modification / prediction / Radiation / Regression models / Remote sensing / remote sensing data / Sensors / surface temperature / Temperature / urban air temperature / Urban areas / urban heat / Variables / Vegetation / Weather / Weather stations / Wind speed