Tepanosyan, G.; Asmaryan, S.; Muradyan, V.; Avetisyan, R.; Hovsepyan, A.; Khlghatyan, A.; Ayvazyan, G.; Dell’Acqua, F. Machine Learning-Based Modeling of Air Temperature in the Complex Environment of Yerevan City, Armenia. Remote Sens.2023, 15, 2795.
Tepanosyan, G.; Asmaryan, S.; Muradyan, V.; Avetisyan, R.; Hovsepyan, A.; Khlghatyan, A.; Ayvazyan, G.; Dell’Acqua, F. Machine Learning-Based Modeling of Air Temperature in the Complex Environment of Yerevan City, Armenia. Remote Sens. 2023, 15, 2795.
Tepanosyan, G.; Asmaryan, S.; Muradyan, V.; Avetisyan, R.; Hovsepyan, A.; Khlghatyan, A.; Ayvazyan, G.; Dell’Acqua, F. Machine Learning-Based Modeling of Air Temperature in the Complex Environment of Yerevan City, Armenia. Remote Sens.2023, 15, 2795.
Tepanosyan, G.; Asmaryan, S.; Muradyan, V.; Avetisyan, R.; Hovsepyan, A.; Khlghatyan, A.; Ayvazyan, G.; Dell’Acqua, F. Machine Learning-Based Modeling of Air Temperature in the Complex Environment of Yerevan City, Armenia. Remote Sens. 2023, 15, 2795.
Abstract
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 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 be strongly impacting on Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between 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, and the use of non-parametric ML techniques.
Keywords
urban air temperature; land surface temperature; multiple independent variables; urban heat; remote sensing data; machine learning (ML); ML-driven partial least squares regression (PLSR)
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
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