Prediction of Karst Suitable Area Using Fuzzy AHP Method and Dempster‐Shafer Theory
Abstract Finding abundant sources of drinking water is a must in practically every country throughout the world. The issue is even more dire in arid and semi‐arid regions, such as Iran, where the shortage of healthy drinking water is quite serious. Finding and maintaining groundwater resources is th...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
American Geophysical Union (AGU)
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/81ff518c0d2e4150bcbbe7156fd2a0ae |
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| Sumario: | Abstract Finding abundant sources of drinking water is a must in practically every country throughout the world. The issue is even more dire in arid and semi‐arid regions, such as Iran, where the shortage of healthy drinking water is quite serious. Finding and maintaining groundwater resources is therefore potentially the best alternative for supplying the water demand of the country. Accordingly, this study aims to assess the potential of karstic areas as a major source of water supply in southwestern regions of the Maharlou watershed, Iran. For this purpose, a combination of fuzzy‐AHP (analytical hierarchy process) and DST (Dempster‐Shafer Theory) methods are used to locate suitable karstic regions in the study area. Karstic zone maps were also generated using a variety of parameters including lithology, fault, precipitation, elevation, temperature, river and slope. Fuzzy maps were also produced using fuzzy membership functions for each parameter, after which the AHP approach was incorporated to assign weights to each layer and eventually generate the final fuzzy‐AHP map. Ultimately, the DST model was used at three confidence intervals of 99.5%, 99%, and 95%. Based on the final results of the fuzzy‐AHP method, approximately 30% of the study region was unsuitable in terms of access to karstic areas, whereas unsuitable zones obtained using the DST method contributed to about 41%, 44%, and 46% of the study region at 95%, 99%, and 99.5% confidence levels, respectively. Taking into account the fact that reductions in the distance from faults increases the probability of the presence of karst regions (with other parameters being constant), by comparing the results of the two models with fault maps, it was determined that the DST model achieves higher accuracy. The DST method is among common methods employed to predict suitable karstic regions, which ultimately produces different maps based on the configured confidence level. |
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