ARTICLE INFO

Article Type

Original Research

Authors

Yahyaei   M. (1)
Mofidi Shemirani   SM. (*2)
Ahmadi   V. (1)






(*2) Department of Architecture, Fac- ulty of Architecture and Urbanism, Iran University of Science & Technology, Tehran, Iran
(1) Department of Architecture, Faculty of Arts and Architecture, Mashhad Branch, Islamic Azad University, Mashhad, Iran

Correspondence


Article History

Received:   September  4, 2020
Accepted:   October 20, 2020
ePublished:   June 16, 2021

ABSTRACT

Aims & Backgrounds The purpose of this study was to locate wind-catcher sites in regions with hot and arid climates in the country (Semnan province).
Methodology The current study is empirical in terms of purpose. First, the initial list of indicators affecting the location of the wind-catchers was prepared by reviewing the fundamental theories was approved by the Delphi method. Then, a combined model was used to find the potential areas for wind-catchers establishment. This model includes the Hierarchical Analysis Process (AHP) technique and the Geographic Information System (GIS). The tools used for this purpose were ArcGIS software, version 9.3, and Expert Choice software, version 2004. The pair comparison method was used to weigh the indicators and zones.
Findings Six indicators of sunny hours, wind, altitude, temperature, relative humidity, and precipitation were identified to locate the win-catchers. The relative humidity index with a weight of 0.38 had the highest score and the height index with a weight of 0.154 had the lowest score. The layer for each indicator was prepared and drawn for the eight cities of the province. Eventually, according to the scores estimated by the pairwise comparison of the indicators and the studied areas, the priorities of the cities of Semnan province in terms of the wind-catchers establishment are as follows: Semnan, Garmsar, Aradan, Sorkheh, Damghan, Shahroud, Miami and Mehdishahr.
Conclusion The optimal locating of wind-catchers requires a combination of several indicators that was possible using the hierarchical analysis method combined with GIS. The final model is practical and generalizable to similar climates.


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