Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

Miguel Núñez-Peiró, Anna Mavrogianni, Phil Symonds, Carmen Sánchez-Guevara Sánchez and F. Javier Neila González
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Miguel Núñez-Peiró: School of Architecture, Universidad Politécnica de Madrid, Avda. Juan de Herrera 4, 28040 Madrid, Spain
Anna Mavrogianni: Institute of Environmental Design and Engineering, University College London, Central House, 14 Woburn Place, London WC1H 0NN, UK
Phil Symonds: Institute of Environmental Design and Engineering, University College London, Central House, 14 Woburn Place, London WC1H 0NN, UK
Carmen Sánchez-Guevara Sánchez: School of Architecture, Universidad Politécnica de Madrid, Avda. Juan de Herrera 4, 28040 Madrid, Spain
F. Javier Neila González: School of Architecture, Universidad Politécnica de Madrid, Avda. Juan de Herrera 4, 28040 Madrid, Spain

Sustainability, 2021, vol. 13, issue 15, 1-23

Abstract: In the last decades, urban climate researchers have highlighted the need for a reliable provision of meteorological data in the local urban context. Several efforts have been made in this direction using Artificial Neural Networks (ANN), demonstrating that they are an accurate alternative to numerical approaches when modelling large time series. However, existing approaches are varied, and it is unclear how much data are needed to train them. This study explores whether the need for training data can be reduced without overly compromising model accuracy, and if model reliability can be increased by selecting the UHI intensity as the main model output instead of air temperature. These two approaches were compared using a common ANN configuration and under different data availability scenarios. Results show that reducing the training dataset from 12 to 9 or even 6 months would still produce reliable results, particularly if the UHI intensity is used. The latter proved to be more effective than the temperature approach under most training scenarios, with an average RMSE improvement of 16.4% when using only 3 months of data. These findings have important implications for urban climate research as they can potentially reduce the duration and cost of field measurement campaigns.

Keywords: urban heat island; microclimate; feed-forward neural networks; air temperature measurements; in-situ measurements; urban models; urban environment; climate change (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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