The Determination of the Developed Karst Regions Using Fuzzy Logic and OWA Models in Qaresou Basin

Wide areas of dry and non-glacier lands of the earth are covered with carbonate formations prone to karstic. Indeed, about 20 to 25% of the world's population live in karstic areas or get their water requirement from karstic water resources. Karstic areas are important because of the following reasons. Firstly, they have an important role in providing and feeding aquifers. The karstic aquifers have also high heterogeneity and spatial diversity in terms of the development of karstic. In addition, they are formed in various forms and developed in karstic regions. For regions such as Iran which does not have enough water resources and 11% of its area covered with karstic, the issue is important. Due to the complex nature of karstic system, especially from the perspective of geomorphology, no models have been offered upon which all aspects of the system can be investigated. Due to the sensitive nature of a karstic system, in the planning of karstic areas, efforts have been made to develop the rate of change and sensitivity of karstic within the framework of the appropriate model or models. Accordingly, in this study using the OWA, fuzzy logic, and network analysis (ANP), the development of the processes and karstic aquifers are discussed in the Qaresou basin. This research was based on field, instrumental, and library techniques. Firstly, using the topographic maps, the basin area of ​​the study was determined. The main data of the research were topographic maps of 1: 50000, geological maps of 1:100,000 and satellite imagery. In this research, 8 factors including lithology, fault, slope, aspect, elevation, river, rainfall, and climate have been used to determine the areas susceptible to Karstic development in the Qaresou Basin. For this purpose, the first step was to prepare information layers using some software. In the last step, considering the parameters, the potential of the region for the development of the processes and the development of karstic aquifers was evaluated. For this purpose, two models of fuzzy logic and sequential weighted averaging (OWA), as well as an analysis network (ANP) model, were used for the zoning and weighting the information layers. The 8 parameters of elevation, slope, aspect, lithology, fault, climate, rainfall, and rivers were used in order to evaluate the development of karstic in the study area. In the lithology and climatic condition layers, the first class had the lowest value in the development of karstic regions and the upper classes had the highest value. In the lithology layer, the first class had formations which had less potential for permeability and the sixth class was calcareous layer with a great potential in this regard. In the climate classes, the first class had a semi-arid climate and had a low score, but the fifth class had a wet climate with a high score. The distance from faults and rivers showed that the areas near faults had a high potential to develop the karstic processes. The pattern of the slope and aspect indicated that the regions with the lowest slope as well as aspects to the north had the highest score. The pattern of the height and precipitation also suggested that areas with significant elevation and more precipitation had great potential in the development of the karstic processes. The pattern of distance from the river showed that the areas away from the river had higher potential and score. Considering the parameters that were considered in order to evaluate the development of the processes and the karstic aquifers, as well as considering the criteria and sub-criteria, the final maps were obtained. Based on the results of the effective factors in the development of the Karstic, the studied basin is divided into five levels of high, relatively high, moderate, low, and very low development.
The evaluation of the final maps indicated that in both maps of the north and northeast regions of the basin there is more development. In addition, due to the favorable climatic, geomorphological, and geological conditions, a large part of the region has a high development that is why the studied basin has a high potential for karstic water resources. Considering that there are differences in fuzzy logic and sequential weighted average (OWA) methods in integrating and combining information layers, the final results have differences in terms of class size. Indeed, in the OWA method because there is more moderation, the difference between the classes is less than the fuzzy logic method, but the general trend of the development in both methods is almost consistent and the extent of development reduces from the north and northeast to the south and southwest.