International Journal of Urban Management and Energy Sustainability

International Journal of Urban Management and Energy Sustainability

Assessing the Influence of Climate on a Framework for Urban Neighborhood Site Selection under a Sustainable-Development Approach Using Artificial Neural Networks and Exploratory Factor Analysis (Case Study: Tehran City, Iran)

Document Type : Case Study

Authors
Zoherh Rezaiekavanroodi
Yahya Kazemi
Department of Civil Engineering, Fir.C., Islamic Azad University, Firoozabad, Iran
10.22034/ijumes.2026.738954
Abstract
Site selection for urban neighborhoods is a multi-criterion, spatially explicit problem that becomes markedly more sensitive under a changing climate, where urban heat island intensity and outdoor thermal comfort strongly condition livability and sustainability. This study develops and demonstrates an integrated framework that couples Geographic Information Systems, exploratory factor analysis and artificial neural networks to assess how climatic factors with an emphasis on UHI and thermal comfort shape a sustainable-development-oriented framework for neighborhood site selection. A pool of climatic, environmental, social, economic and physical–access indicators are assembled from GIS layers at the neighborhood scale. EFA reduces the correlated indicators to a small set of interpretable latent factors. The factor scores then serve as inputs to a multilayer-perceptron ANN trained to predict a neighborhood sustainability suitability index, which is validated with R², RMSE and MAE and interrogated with a sensitivity analysis. In the demonstrative case, EFA retained four factors, climate & thermal, access & mobility, socio-economic, and physical form explaining a large share of the variance, with the climate & thermal factor. The ANN reproduced the reference suitability index with high accuracy on the held-out set, and the sensitivity analysis ranked UHI-related variables as the most influential. Neighborhoods were classified into five suitability tiers. All of these values are synthetic and illustrate the framework’s behavior. Coupling EFA with ANN inside a GIS pipeline provides a transparent, reproducible and transferable framework for climate-sensitive, sustainability-oriented neighborhood site selection, and foregrounds UHI mitigation as a first-order siting criterion. The framework is ready to be populated with empirical Tehran data to produce validated, city-specific results.

Graphical Abstract

Assessing the Influence of Climate on a Framework for Urban Neighborhood Site Selection under a Sustainable-Development Approach Using Artificial Neural Networks and Exploratory Factor Analysis (Case Study: Tehran City, Iran)

Highlights

  • A reproducible GIS–factor-analysis–ANN framework is proposed for climate-sensitive, sustainability-oriented urban neighborhood site selection.
  • Exploratory factor analysis reduces correlated environmental, social, economic and physical indicators to four interpretable latent factors.
  • A multilayer-perceptron neural network learns the non-linear mapping from the factors to a neighborhood sustainability suitability index.
  • Urban heat island variables land surface temperature, green cover and sky view factor emerge as first-order siting determinants in the illustrative case.
  • Demonstrated on Tehran neighborhoods with a clearly-labelled synthetic dataset, the transferable framework is ready to be populated with empirical GIS data

Keywords
Artificial neural network
exploratory factor analysis
site selection
sustainable development
urban heat island
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  • Receive Date 12 January 2026
  • Revise Date 09 February 2026
  • Accept Date 13 April 2026