Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid Article Swipe
Miguel Núñez Peiró
,
Anna Mavrogianni
,
P. Symonds
,
Carmen Sánchez-Guevara Sánchez
,
Fco. Javier Neila González
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/su13158143
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/su13158143
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.
Related Topics
Concepts
Artificial neural network
Context (archaeology)
Training (meteorology)
Term (time)
Reliability (semiconductor)
Computer science
Urban heat island
Field (mathematics)
Mean squared error
Machine learning
Environmental science
Meteorology
Statistics
Geography
Mathematics
Archaeology
Physics
Quantum mechanics
Power (physics)
Pure mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su13158143
- https://www.mdpi.com/2071-1050/13/15/8143/pdf?version=1626866031
- OA Status
- gold
- Cited By
- 3
- References
- 143
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3185297269
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3185297269Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/su13158143Digital Object Identifier
- Title
-
Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of MadridWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-21Full publication date if available
- Authors
-
Miguel Núñez Peiró, Anna Mavrogianni, P. Symonds, Carmen Sánchez-Guevara Sánchez, Fco. Javier Neila GonzálezList of authors in order
- Landing page
-
https://doi.org/10.3390/su13158143Publisher landing page
- PDF URL
-
https://www.mdpi.com/2071-1050/13/15/8143/pdf?version=1626866031Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2071-1050/13/15/8143/pdf?version=1626866031Direct OA link when available
- Concepts
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Artificial neural network, Context (archaeology), Training (meteorology), Term (time), Reliability (semiconductor), Computer science, Urban heat island, Field (mathematics), Mean squared error, Machine learning, Environmental science, Meteorology, Statistics, Geography, Mathematics, Archaeology, Physics, Quantum mechanics, Power (physics), Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
143Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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