The Application of Hierarchical Clustering and Principal Components Analysis in Microclimate Zoning of Central Iran

Khoramabadi, Farahnaz; Bafandeh, Nahid; Ghaffari, Alireza; Panahi, Ali

doi:10.22034/gahr.2025.556133.2616

The Application of Hierarchical Clustering and Principal Components Analysis in Microclimate Zoning of Central Iran

Articles in Press

Document Type : Original Article

Authors

farahnaz khoramabadi ¹

Nahid Bafandeh ²

Alireza Ghaffari ³

Ali Panahi ⁴

¹ University of Isfahan, Faculty of Geography and Planning

² Zanjan and the University of Zanjan

³ University of Pune

⁴ Tabriz Azad University

10.22034/gahr.2025.556133.2616

Abstract

Consequently, a precise understanding of climatic patterns is essential for various planning activities, including those in the economic, agricultural, and industrial sectors.

This study employed multivariate statistical methods to identify climatic sub-regions in Central Iran. Data from 15 meteorological stations over specified statistical periods were collected and analyzed. Zoning maps were prepared using ArcGIS software.

The objective of this research was to identify climatic sub-regions in the study area using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis. The use of multivariate statistical methods proved to be an effective tool for determining these climatic sub-regions. The results revealed that two factors, precipitation and maximum temperature, play the most significant role in creating climatic diversity in Central Iran.

Together, these two factors account for 73.57% of the climatic variation in the region. By performing Hierarchical Cluster Analysis using Ward's linkage method on the matrix of factor scores, four main zones and their sub-regions were identified. The findings of this study can be useful for regional and local planning in various fields, including agriculture, water resources, and urban management.

Keywords

Homogeneous Climate Zones

Hierarchical Cluster Analysis - HCA

Central Iran

Subjects