A Machine Learning Approach for the Efficient Estimation of Ground-Level Air Temperature in Urban Areas

TL;DR

This paper demonstrates that deep neural networks can efficiently estimate street-level air temperature in urban areas, providing a faster and less computationally intensive alternative to traditional numerical models, aiding urban heat island mitigation.

Contribution

It introduces a novel application of image-to-image deep neural networks for accurate, efficient estimation of ground-level air temperature in cities, improving urban heat management.

Findings

DNNs are faster than numerical models for temperature estimation.

DNNs require less computational resources.

Results show high correlation with established models.

Abstract

The increasingly populated cities of the 21st Century face the challenge of being sustainable and resilient spaces for their inhabitants. However, climate change, among other problems, makes these objectives difficult to achieve. The Urban Heat Island (UHI) phenomenon that occurs in cities, increasing their thermal stress, is one of the stumbling blocks to achieve a more sustainable city. The ability to estimate temperatures with a high degree of accuracy allows for the identification of the highest priority areas in cities where urban improvements need to be made to reduce thermal discomfort. In this work we explore the usefulness of image-to-image deep neural networks (DNNs) for correlating spatial and meteorological variables of a urban area with street-level air temperature. The air temperature at street-level is estimated both spatially and temporally for a specific use case, and compared with existing, well-established numerical models. Based on the obtained results, deep neural networks are confirmed to be faster and less computationally expensive alternative for ground-level air temperature compared to numerical models.