ABSTRACT:Landslides are among the most important natural hazards that lead to modification of the environment. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic, and geomorphologic characteristics of the region, the purpose of this study was landslide hazard assessment using Fuzzy Logic, frequency ratio and Analytical Hierarchy Process method in Dozein basin, Iran. At first, landslides occurred in Dozein basin were identified using aerial photos and field studies. The influenced landslide parameters that were used in this study including slope, aspect, elevation, lithology, precipitation, land cover, distance from fault, distance from road and distance from river were obtained from different sources and maps. Using these factors and the identified landslide, the fuzzy membership values were calculated by frequency ratio. Then to account for the importance of each of the factors in the landslide susceptibility, weights of each factor were determined based on questionnaire and AHP method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method. At the end, for computing prediction accuracy, the produced map was verified by comparing to existing landslide locations. These results indicate that the combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility map about 51% of the occurred landslide fall into the high and very high susceptibility zones of the landslide susceptibility map, but approximately 26 % of them indeed located in the low and very low susceptibility zones.

One of the main challenges of human is the dramatic decrease in resources due to human"s excessive consumption of land that has led to a phenomenon called land degradation. Various models have thus far been introduced for assessment of this phenomenon. The parameters and their weights differ from one model to another as per experts" opinion. The present study introduces a new method to identify and calibrate the parameters, as per the conditions of the region under study, affecting this phenomenon. The proposed method is considered as a data-based model such that parameter weights are computed intelligently and as per the climate and geographical conditions of the region. The genetic algorithm and Weighted Overlay Index were used to determine the significance level and ranking of the criteria. For the purpose of assessment, the data pertaining to Neinava region, located in Iraq, including Landsat satellite images of 1985, 2001, and 2014 as well as criteria such as distance from rivers, distance from lakes, distance from agricultural areas, distance from roads, distance from residential areas, height, slope, distance from Qanats, distance from wells, erosion, type of climate, and NDVI index were used. The results obtained from modeling and calibration as per the proposed model were compared with those of the regular method (application of equal weights). Application of genetic algorithm and calibration of weights yielded a standard deviation of 0.03 for prediction of vegetation degradation which is considerably lower than that yielded by the regular method (0.137). The criteria were also prioritized at this stage as per their significance. To ensure the model accuracy, data of 2001 and 2014 were used to assess the obtained results. The assessment result yielded a standard deviation of 0.053 and accuracy of 0.857. After the accuracy of the model was ensured, the vegetation degradation was predicted for 2027. The average rate of decreased NDVI values indicates the critical status of land degradation in the region under study.

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