Volume 1, Issue 3 (Autumn 2020 2020)                   JGSMA 2020, 1(3): 67-80 | Back to browse issues page


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Maleki M, Jozak A, Sadidi J. Identification of Sinkhole Prone Areas in Biston-Paro Karst Basin. JGSMA 2020; 1 (3) :67-80
URL: http://gsma.lu.ac.ir/article-1-72-en.html
1- M.Sc. of Remote Sensing and GIS Department, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran. , malekimohamad14@gmail.com
2- M.Sc. Student of Geography and Urban Planning, Faculty of Humanities and Social Sciences, Mazandaran University, Babol Sar, Iran.
3- Assistant Professor of Remote Sensing and GIS Department, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran.
Abstract:   (3939 Views)
Sinkhole is a type of karst form that appear as pits on the surface of the earth. Identification of these sinkholes is crucial in the management of water resources, as contamination of these sites causes contamination of water resources in the region. Bistoon-Parav Karst Basin is important because it creates mirages in the cities of Bistoon and Kermanshah and provides part of the water to these cities. The purpose of this study was to identify sinkholes in this area as well as identify potential areas of sinkholes. In this research, using the analysis network process, different criteria for sinkhole formation such as precipitation, temperature, evaporation, lithology, soil type, slope, elevation, fault, stream and vegetation are ranked. The results of the analysis network process showed that lithology with 24.87% are the most important cause of the sinkhole. After combining the layers, a map of the possible sinkhole areas was identified. Furthermore, sinkholes in the area were detected using the interpretation of World Imagery and Google Earth imagery. The accuracy, completeness and quality indices were used to evaluate the results of the work, which were 98.42, 69.41 and 65.55, respectively. The high accuracy index indicates high performance in detecting existing sinkholes, but the low two indices do not indicate the weakness of the method, rather, the two indicators of completeness and quality indicate areas that are likely to form sinkholes. However, at the moment they are either not crashed or they are not in the reference data. In the end it can be said that this method is suitable for identifying sinkholes and susceptible areas of sinkholes.
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Type of Study: Research | Subject: Special
Received: 2020/11/20 | Accepted: 2020/12/19

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