Prioritization of potential areas for construction of underground dam using geometric average method in geographical information system

Document Type : Research Article

Authors

1 Assistant Professor, Faculty of Engineering, Malayer University, Malayer, Iran

2 Assistant Professor, Department of Watershed and Rangeland Management, Malayer University, Malayer, Iran

3 MA Student in Watershed Management, Malayer University, Malayer, Iran

Abstract

Exploitation of underground water resources is a way to modulate the shortage of seasonal water. In this regard, proper site selection for constructing underground dam to reserve waters is a challenging issue. Thus, using an approach with minimum error is essential. The purpose of this paper was optimum selection of potential sites for constructing underground dam in Hamedan-Bahar catchment basin using a new GIS-based approach to reduce the error. For this purpose, the efficient criteria of a proper site to make underground dam were first recognized. These criteria include density of drainages, separation of the aqueducts, spring and wells, geological appropriate, slope, fault density, proximity to roads and villages and special conditions of land use. Then, different evidence layers were weighted in [0, 1] range using logistic function in geographic information system. Finally, the weighted evidence layers were integrated using geometric average function. Thus, a model representing favorable areas for making underground dam was generated. The results obtained introduced less than 10% of the streams (respecting the whole streams in the study area) as suitable, and demonstrated that the method applied can be used efficiently to delimit the study area and to recognize suitable streams for construction of underground dam.

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Main Subjects

References

منابع
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Iranian journal of Ecohydrology
Volume 4, Issue 3
September 2017
Pages 663-672

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History

  • Receive Date: 14 October 2016
  • Revise Date: 14 March 2017
  • Accept Date: 15 March 2017
  • First Publish Date: 23 September 2017
  • Publish Date: 23 September 2017

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