Document Type : Original Article
Assistant professor of Amin police university
Department of Remote Sensing and GIS, Kharazmi University, Tehran, Iran
Department of surveing engineering,marand technical college,university of tabriz,tabriz, iran,
Department of Geography and Urban Planning, Islamic Azad University, Tehran, Iran
One of the most common surface features of Karst topography is sinkholes. The karst areas provide drinking water for 25% of the world’s population. Identifying sinkholes is crucial in managing water resources, as their contamination leads to the contamination of water resources in the area. The Bisotun-Parav Karstic Basin is essential because it creates spring wells in Bisotun and Kermanshah and supplies part of their water. This study aims to detect potential areas for sinkholes using GIS and Decision Making Trial and Evaluation Laboratory)-based analytic network process (DANP). The criteria which were used are Climatology (precipitation, temperature, evaporation, streams), Topography (slope, elevation), Agriculture(vegetation), Lithology (lithology, soil type, fault). Then the required layers were obtained, and the importance of each factor was determined through a combination of the DEMATEL technique and the ANP. Finally, after combining the layers, a map of potential sinkhole areas was obtained. Sinkholes in the area were detected using the visual interpretation of world imagery and google earth imagery as reference data. The results of the DANP demonstrated vegetation, elevation, and lithology with the value of 22.59%, 12.12%, and 11.94 respectively are the most important factors involved in the formation of sinkholes. The indexes of correctness, completeness, and quality were then used to evaluate the study results and turned out to be 98.73%, 79.86%, and 79%, respectively. The high correctness index indicates high efficacy in detecting the existing sinkholes, but the low percentage of the other two indexes does not indicate the inefficacy of the method; rather, the two indexes of completeness and quality indicate areas with a potential for sinkhole formation that either has no sinkholes or are not in the reference data. This method effectively detects sinkholes and potential areas for sinkhole formation.