Mahdi Alirezazadeh Alirezazadeh; Mahmood Heydarian; Alireza Khosrowzadeh
Abstract
Archaeologists have identified and recorded only some of the innumerable archaeological sites, while thousands of sites are destroyed each year to make way for ongoing land development. ...
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Archaeologists have identified and recorded only some of the innumerable archaeological sites, while thousands of sites are destroyed each year to make way for ongoing land development. One way to help us understand and protect these sites is to create Predictive models. It is obvious that when it comes to modeling, in fact, with Reductionist approaches, it is an attempt to simulate a phenomenon that has occurred in very distant times. However, predictive models are tools that can help archaeologists in many cases. This research tries to introduce the most suitable locations for the formation of the Chalcolithic settlements of Chaharmahal and Bakhtiari whit ArcMap software and based on utilizing the relationship between ancient settlements and landscapes, investigating the role of factors affecting these settlements and predictive models. To achieve this, at first, 75 Chalcolithic sites in this area were surveyed relative to the environmental variables of altitude from the sea level, slope percent, distance from the water resources, vegetation, and soil texture class. Then using fuzzy logic analysis in GIS, the most suitable places for the existence of Chalcolithic sites were identified. Finally, after analyzing the situation of the spatial distribution of the sites concerning the factors mentioned above, and examining the accuracy of the proposed model based on using 15 control sites, proved that this model has come up well from the forecast of the most suitable locations for the formation of the Chalcolithic settlements of Chaharmahal and Bakhtiari. In addition to saving time and money, such models can help to better understand the environmental potentials and factors influencing the formation of prehistoric sites. It is very clear that in archeological surveys, a process similar to modeling is subconsciously formed in the mind of the survey team so that after a few days of fieldwork in a particular landscape, the survey team subconsciously understands in which part of the environment the "possibility" of the site is more and in which parts it is less. It should be noted that environmental factors affecting ancient settlements are diverse and abundant. But some of them can be selected as the most important factors. It can never involve all the influencing factors in modeling. The nature of modeling takes advantage of this reductionist perspective. In the meantime, to provide a predictive model for Chalcolithic sites of Chaharmahal and Bakhtiari, 75 Chalcolithic sites in the region are used. These sites have been identified in archaeological surveys in the region. However, from the environmental variables, five variables of Altitude, Percentage of Land Slope, Distance from the Waterway, Vegetation, and Soil Texture Class were selected. Another point involved in selecting variables is that they do not change much over time. The way it works is that these five variables are added information layers in the ArcMap software. Then the geographical UTM of the desired sites will be added to each of these layers separately. It is easy to determine the distribution of the Chalcolithic sites of the region concerning these layers. These distributions will be presented in the form of diagrams. Here the required information is extracted from 75 sites. This information extracted in the next step forms the basis for the production of “Fuzzy” layers. These fuzzy layers are made with the help of the extracted data of the previous step and by selecting the desired Fuzzy Functions. Finally, by overlay the five fuzzy layers produced, a prediction model is presented for the Chalcolithic sites of the area. The questions that arise here are how ArcMap software can help to better understand environmental factors on ancient settlements? What are the advantages of fuzzy logic modeling? Are such proposed models effective in practice or are they purely theoretical? How accurate is this model and where are the position of important sites and regional indicators in this model? It should be noted that ArcMap software has many capabilities. Among other things, it can easily provide researchers with a view of an area with the help of a digital elevation model (DEM). Using DEM, the location of the sites to environmental factors can be determined. Suppose you have identified a large number of sites, with the help of this software you can easily and with a few commands to determine what distribution the sites have taken with rivers and other factors. These are just a few of the many features of ArcMap software. Using this software, different layers can be analyzed and models can be presented. One of the advantages of the fuzzy prediction model for researchers is that it provides infinite continuous values for the possibility of having a site in a landscape. That is, based on the input information, it can determine the possibility of a site in a landscape as a continuous spectrum (from zero to one). Therefore, the study area can be divided into very desirable, desirable, moderate, etc. in terms of the possibility of the existence of an ancient site. This capability is due to the continuous quantification of fuzzy logic to each of these environmental factors. Also, the prediction model presented in this method is testable. That is, if a model is presented based on the number of sites, it can be tested by several other sites. It can even be determined in the model where the position of the index sites of the region is and what number this model considers for the geographical location of those sites. In fuzzy logic modeling, the performance of fuzzy functions plays a key role. The proposed model can only provide a near-realistic explanation if these functions are used correctly. For example, it is very obvious that in the analysis of variables of altitude and percentage of land slope, different fuzzy functions should be used. As the following theoretical studies will show, the percentage of slope changes compared to altitude changes are more decisive in determining the location of the site. That is, if a few percent is added to the slope, this factor moves to the critical point, while the altitude variable is not so sensitive. That is, in this particular example, the fuzzy function for the slope of the land must be more sensitive than the fuzzy function for the altitude variable. Therefore, it is necessary to model each of the variables with the most appropriate fuzzy function available. That is, the sensitivity of the variable is in good agreement with the sensitivity of the fuzzy function. Considering all these cases, a fuzzy prediction model was presented for the location of Chalcolithic sites of Chaharmahal and Bakhtiari. In this model, it was determined what distribution the sites of the region have adopted concerning the mentioned five variables. The desired geography was determined for the possibility of the existence of Chalcolithic sites in this area. In this model, the entire surface of the area was presented as a map, which is colored with a range of black (zero) to white (one). White areas are desirable places for the existence of Chalcolithic sites in the region. However, the black dots indicate that the possibility of a site in those areas is very low. Significant sites of the region, such as Choghate Eskandari, Tape Jamalo, Koganak, Aloni, Tape Afghan, Gerde Chelehgah, and the like, are in white dots in this model. Also, the fuzzy value of these areas can be determined with great accuracy, as shown in the table in this text. Therefore, by using these models, it is possible to determine the desired landscape for placing the Chalcolithic site in practice, not just on paper. While the importance of environmental factors involved in this modeling can be seen. This modeling can save time and money. Also, the reasons for the formation of sites in different landscapes can be examined, and vice versa, why the distribution and existence of sites in such parts of the study area are so low.
