Javad Sadidi; Reza Maliki
Abstract
In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data ...
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In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.
Remote sensing and Gis
fatemeh Adimi Atigh
Abstract
: Over time, patterns of land cover and land use change and subsequent changes are fundamental and human factor plays a most important role in this process. Ever, scientists have attempted to identify factors that cause land use changes and their impact on the environment. Therefore, in previous decades, ...
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: Over time, patterns of land cover and land use change and subsequent changes are fundamental and human factor plays a most important role in this process. Ever, scientists have attempted to identify factors that cause land use changes and their impact on the environment. Therefore, in previous decades, researchers have different views collected from the field, as well as aerial photographs to detect land use changes resulting from the imposition of natural and human processes have been analyzed. Today, however, based on technological advances made in the field of remote sensing, satellite imagery can be used to more accurately evaluate the environmental changes during the process and the final results of the illustrated model. The main purpose of the ongoing monitoring of land use changes in river basins liqvan is 1985-2006-2013. Accordingly, to explore the changes occurring in the study area, Landsat TM and ETM + Landsat images of the years 1985-2006-2013 were analyzed. Accordingly, after applying atmospheric and geometric correction, image enhancement operations performed using the maximum likelihood method of supervised classification algorithms similar actions and thematic maps of land use of the basin has been designed to liqvan. In general, the overall accuracy of SVM method calculated in 1985 (96.20) and in 2006 (96.26) and in 2013 (99.64) by Mdkh highest accuracy than other methods. Finally moorland in the first place and then irrigated gardens and residential areas in the study area are eventually.
