Explaining an Optimal Location Model for Desirable Housing in Tabriz Based on a Zero-Carbon Approach Using an Artificial Neural Network in a GIS Environment (Case Study: Tabriz Metropolis)

Motallebi, Sharareh; Saghafi Asl, Arash; Shahhoseini, Habib; Haghlesan, Masoud

doi:10.22034/ijumes.2026.2091582.1375

Explaining an Optimal Location Model for Desirable Housing in Tabriz Based on a Zero-Carbon Approach Using an Artificial Neural Network in a GIS Environment (Case Study: Tabriz Metropolis)

Articles in Press

Document Type : Case Study

Authors

Sharareh Motallebi ¹

Arash Saghafi Asl ¹

Habib Shahhoseini ¹

Masoud Haghlesan ²

¹ Department of Architecture and Urban Planning, Ta.C., Islamic Azad University, Tabriz, Iran

² Department of Architecture and Urban Planning, Ilk.C., Islamic Azad University, Ilkhchi, Iran

10.22034/ijumes.2026.2091582.1375

Abstract

The location of new housing is among the most carbon-consequential decisions a city makes, because where people live largely determines how far and by what mode they must travel and how much energy their daily lives consume. The objective of this research is therefore to develop and validate an operational model for the optimal location of desirable housing in Tabriz under a zero-carbon approach, by integrating an Artificial Neural Network of the multilayer-perceptron type with a Geographic Information System spatial database. Adopting an analytical–applied design within a post-positivist paradigm, eight carbon-relevant location criteria capturing walkable service access, public-transport proximity, mixed-use intensity, green-space access, education proximity, residential infill, industrial-emission buffering, and river/flood buffering were derived directly from an official municipal land-use layer through GIS spatial operations and used to train a back-propagation network, with performance evaluated against held-out validation and test data and benchmarked against a conventional weighted-linear baseline. Findings indicate that the trained network achieved a coefficient of determination of 0.951 and a root-mean-square error of 0.040 on the validation set, that public-transport proximity, mixed-use intensity, and green-space access were the most influential criteria, and that the highest-suitability housing sites concentrated in the compact, transit- and service-rich central core of the city. The study concludes that ANN–GIS integration under a zero-carbon framing constitutes a robust, interpretable, and transferable instrument for steering housing development toward low-carbon urban form in Iranian metropolitan contexts.

Graphical Abstract

Explaining an Optimal Location Model for Desirable Housing in Tabriz Based on a Zero-Carbon Approach Using an Artificial Neural Network in a GIS Environment (Case Study: Tabriz Metropolis)

Highlights

· An ANN–GIS model was developed to identify optimal zero-carbon housing locations in Tabriz metropolis.

· Eight carbon-relevant spatial criteria were derived from official municipal land-use data using GIS analysis.

· The trained multilayer perceptron achieved high predictive accuracy (R² = 0.951; RMSE = 0.040).

· Public-transport proximity, mixed-use intensity, and green-space access were identified as the most influential suitability factors.

· The proposed ANN–GIS framework provides a transferable decision-support tool for guiding low-carbon urban housing development.

Keywords

Artificial neural network

desirable housing

GIS

multilayer perceptron

optimal location

Tabriz metropolis

Zero-Carbon city

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International Journal of Urban Management and Energy Sustainability

Articles in Press, Accepted Manuscript
Available Online from 01 August 2026

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Receive Date 28 April 2026
Revise Date 14 June 2026
Accept Date 01 August 2026

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International Journal of Urban Management and Energy Sustainability

Explaining an Optimal Location Model for Desirable Housing in Tabriz Based on a Zero-Carbon Approach Using an Artificial Neural Network in a GIS Environment (Case Study: Tabriz Metropolis)

Articles in Press, Accepted Manuscript Available Online from 01 August 2026

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Articles in Press, Accepted Manuscript
Available Online from 01 August 2026