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

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.

Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in Urban Allotment Gardens and an Urban Park in Valladolid, Castilla y León, Spain

“…For this reason, one alternative for determining urban temperatures are to instal in situ sensors throughout the city. Nevertheless, not all municipalities are willing to invest in this approach due to its economic cost and the risks of theft or damage, which limit its suitability for long-term studies [31,35].…”

“…To empirically understand what happens in these urban spaces, researchers in urban climatology have highlighted the need for reliable meteorological data in the local urban context [31]. Generally, the investigations related to the effect of green spaces, such as urban gardens and urban parks, on urban temperatures are based on methods and analyses that involve in situ sensors for long periods (for example, during all the summer) without taking advantage of the innovative predictive capabilities of Artificial Neural Networks (ANNs) [21,29,30,32].…”

“…Generally, the investigations related to the effect of green spaces, such as urban gardens and urban parks, on urban temperatures are based on methods and analyses that involve in situ sensors for long periods (for example, during all the summer) without taking advantage of the innovative predictive capabilities of Artificial Neural Networks (ANNs) [21,29,30,32]. Based on their results and practicality, ANNs offer a valuable tool to understand and address the complex dynamics of urban climates [31,33,34]. However, there is a lack of scientific evidence that develops ANNs for urban temperature focusing on the influence of urban green spaces, such as urban gardens and urban parks, from in situ data.…”