Somayeh Movahedi; aboalhasan fathabadi; null null; Ali Heshmatpour
Abstract
In this study using Frequency Ratio (FR), Statistical Index (SI), Weights Of Evidence(WOF), Logistic Regression (LR), Random Forest (RF) models the probability of gully formation was calculated in Aytamar watershed and susceptibility maps was prepared. First the thematic maps of 13 gully conditioning ...
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In this study using Frequency Ratio (FR), Statistical Index (SI), Weights Of Evidence(WOF), Logistic Regression (LR), Random Forest (RF) models the probability of gully formation was calculated in Aytamar watershed and susceptibility maps was prepared. First the thematic maps of 13 gully conditioning factors including lithological formations, distance to faults, faults density, altitude, slope-length, slope angle, slope aspect, plan curvature, profile curvature, distance to roads, land use, distance to rivers, stream power index and topographic wetness index was prepared. Then landslide inventory map was combined with each gully conditioning factor and all models weights and parameters were calculated. Area under curve for test data was calculated as 0.74, 0.78, 0.75, 0.86 and 0.96 for Frequency Ratio (FR), Statistical Index (SI), Weights Of Evidence(WOF), Logistic Regression (LR), Random Forest (RF) models, respectively. Random forest, Frequency Ratio and Logistic Regression have the least the area of high susceptibility zone, respectively. With respect three validation criteria multivariate methods including Random Forest and Logistic Regression had the best performance among all models.
majid kazemzadeh; arash malekian
Abstract
One of the most important dynamic ecosystems is river, awareness of spatio-temporal water quality changes of which is necessary. In this research, we studied the spatiotemporal water quality changes using three techniques of Cluster analysis (CA), Discriminant analysis (DA) and Principal Component analysis ...
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One of the most important dynamic ecosystems is river, awareness of spatio-temporal water quality changes of which is necessary. In this research, we studied the spatiotemporal water quality changes using three techniques of Cluster analysis (CA), Discriminant analysis (DA) and Principal Component analysis (PCA) in the Aji-Chai watershed over 1981-2010. Applying clustering, we identified three homogeneities clusters. Stations which were labeled in the first cluster showed that they are located in the upstream of Aji-Chi River. In comparison with other stations, these stations showed better water quality and the lowest changeability. DA methods significantly determined the three functions which described about 73.50, 20.30 and 3.40% of total variances. In the other word, in general three functions described the 97.20% of the total variances. Also the DA methods revealed the HCO-3, SAR, Na+, SO42- and Ca2+ were the most important parameters affecting upon water quality, based on which it's possible to seperate homogenous clusters. Finally, the results of PCA showed that the first two factors were the most important factors of water quality changes in the Aji-Chai River Watershed. These factors described about 78.75 and 14.71% of the variances, respectively.
