Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features

• URL: http://arxiv.org/abs/2409.02120v1
• Date: Sat, 31 Aug 2024 12:59:21 GMT
• Title: Machine Learning Framework for High-Resolution Air Temperature Downscaling Using LiDAR-Derived Urban Morphological Features
• Authors: Fatemeh Chajaei, Hossein Bagheri,
• Abstract summary: This study presents a data-driven framework for downscaling air temperature using datasets generated by UrbClim. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. The framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air temperature downscaling. This study presents a data-driven framework for downscaling air temperature using publicly available outputs from urban climate models, specifically datasets generated by UrbClim. The proposed framework utilized morphological features extracted from LiDAR data. To extract urban morphological features, first a three-dimensional building model was created using LiDAR data and deep learning models. Then, these features were integrated with meteorological parameters such as wind, humidity, etc., to downscale air temperature using machine learning algorithms. The results demonstrated that the developed framework effectively extracted urban morphological features from LiDAR data. Deep learning algorithms played a crucial role in generating three-dimensional models for extracting the aforementioned features. Also, the evaluation of air temperature downscaling results using various machine learning models indicated that the LightGBM model had the best performance with an RMSE of 0.352{\deg}K and MAE of 0.215{\deg}K. Furthermore, the examination of final air temperature maps derived from downscaling showed that the developed framework successfully estimated air temperatures at higher resolutions, enabling the identification of local air temperature patterns at street level. The corresponding source codes are available on GitHub: https://github.com/FatemehCh97/Air-Temperature-Downscaling.

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