International Journal of Human Capital in Urban Management
Quarterly Publication

Prediction of land use changes in Hyrcanian forests using an Artificial Neural Network model

https://doi.org/10.22034/IJHCUM.2026.02.06

Articles in Press, Accepted Manuscript
Available Online from 24 November 2025
International Journal of Human Capital in Urban Management

Document Type : ORIGINAL RESEARCH ARTICLE

Authors

M. Jadidi
M. J. Amiri

Faculty of Environment, University of Tehran, Tehran, Iran

Abstract
BACKGROUND AND OBJECTIVES: Land use change is a pressing global environmental crisis requiring scientific study for sustainable regional decisions. This study analyzes the spatial-temporal dynamics of land use in the Hyrcanian forests of western Mazandaran province from 2013-2023 using remote sensing data. Image classification was based on six land use classes: vegetation, built-up, agriculture, water bodies, forest, and bare land.
METHODS: An Artificial Neural Network was employed to predict land use changes over ten years. The model was validated by comparing the simulated 2023 map with the actual map, resulting in a Kappa coefficient of 92%.
FINDINGS: Land use change maps from 2013-2023 show that built-up areas increased by 26.5517 km2, while forest and other vegetation decreased by 43.6353 km2 and 85.1967 km2, respectively. Projections for 2023-2033 indicate similar trends: an increase in built-up areas by 31.3106 km2 and a decrease in forest and other natural areas by 8.875 km2 and 16.6104 km2, respectively.
CONCLUSION: This research offers a valuable tool for the sustainable management of Hyrcanian forests, aiding informed decision-making for environmental improvement, identifying threats, optimal resource management, and predicting the effects of climate change. It offers valuable insights for sustainable planning, management, and improved environmental outcomes.

Keywords

Artificial Neural Network
Change prediction
Hyrcanian forests
Remote sensing
Sustainable management
Subjects
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  • Receive Date 17 May 2025
  • Revise Date 27 July 2025
  • Accept Date 23 November 2025

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