Seid Saeid Ghiasi; Sadat Feiznia; Alireza moghadam nia; Ali Najafinejad; Somayye Najirad
Abstract
Landslide susceptibility assessment is a primary tool for understanding the basic characteristics of slopes that are prone to landslides. In this study, a landslide susceptibility assessment was accomplished, by adopting the Statistical Index Method (SIM) and the Analytic Hierarchy Process (AHP). Ten ...
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Landslide susceptibility assessment is a primary tool for understanding the basic characteristics of slopes that are prone to landslides. In this study, a landslide susceptibility assessment was accomplished, by adopting the Statistical Index Method (SIM) and the Analytic Hierarchy Process (AHP). Ten landslide causing factors were considered including: elevation, slope, aspect, lithology, land use, drainage density, plan curvature, precipitation, geomorphologic faces, and rock unit’s sensitivity to erosion. The SIM was used to determine the weighted value (Si) for classes of every landslide causing factor, the AHP was utilized to determine the weighted value (Wi) for every factor. The summation of the product of Si by Wi represent the landslide Susceptibility Index (LSI) value for every pixels. Based on the derived LSI, landslide susceptibility map (LSM) was produced then the study area was grouped into five susceptibility classes. The densities of landslide for five susceptibility classes implying there is a satisfactory agreement between the susceptibility map and the actual landslide data. In the following, the results of the LSM were quantitatively validated using observed landslide dataset and the receiver operating characteristic (ROC) method. The validation results showed that the AUC for prediction rate of model was 95.2%. The landslide susceptibility showed the areas with lithology of old terraces, young terraces, lahar, and porphyritic trachyandesite-trachyte with different degrees of sensitivity to erosion which distribute between 10–40% slope and more than 60% are very prone to slope failure. Therefore, SIM and AHP were found to be effective models for landslide susceptibility mapping.
Omid Asadi Nalivan; Seid Saeid Ghiasi; Sadat Feiznia; Narges sagghazade
Abstract
At present, Groundwater contamination by nitrate, serves as one of the most important environmental issues. In respect to various land uses of Silveh basin, its ground water quality parameters might vary spatially and temporally. For this, ground water samples taken from 145 points were evaluated. After ...
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At present, Groundwater contamination by nitrate, serves as one of the most important environmental issues. In respect to various land uses of Silveh basin, its ground water quality parameters might vary spatially and temporally. For this, ground water samples taken from 145 points were evaluated. After determining nitrate spatial variations by varyogram, different methods involved distance inverse method and geo-statistics methods of radial estimator approaches, local estimator, ordinary kriging, simple kriging and global kriging were evaluated using GIS software and nitrate spatial distribution map were prepared in two time intervals (pre and post-harvest). Criteria based on the Root Mean Squared Error(RMSE), ordinary kriging method has the lowest error, and the accuracy considerably. Spatial distribution of nitrate in area groundwater indicated that there was high concentration of nitrate in land uses of agriculture and arid area. Of course, presence of shale-stone causes nitrate releases, intensifying issues. Comparison of nitrate samples concentration with national and international standards suggested that 1.38%(2 Point) of all samples have been nitrate-contaminated before harvesting, while 11.03%(16 Point) of them have been contaminated after harvesting.
