Kourosh Shirani; Reza Naderi Samani
Abstract
The aim of this study is to prioritize effective factors, landslide susceptibility zonation assessment using maximum entropy (MaxEnt) and dempster shafer models in Doab Samsami watershed of Chaharmahal and Bakhtiari province. For this purpose, 15 factor maps affecting landslide occurrence as independent ...
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The aim of this study is to prioritize effective factors, landslide susceptibility zonation assessment using maximum entropy (MaxEnt) and dempster shafer models in Doab Samsami watershed of Chaharmahal and Bakhtiari province. For this purpose, 15 factor maps affecting landslide occurrence as independent variables and landslide distribution map as a dependent variable were prepared and weighted using frequency ratio index (FR) and landslide distribution map in the environment ArcGIS® 10.8 . In order to implementation and validation of models, landslide distribution data were randomly divided into two categories of training and test data in the proportion of 70 and 30%, respectively. Maximum Entropy (MAXENT) and Dempster Shaffer models are performed and landslide susceptibility zonation maps are prepared and each model is divided into five very low to very high. In order to evaluate the classification accuracy and validation of the models, the frequency ratio and seed cell area index (FR&SCAI) and the area under receiver characteristic curve (AUC-ROC) were used, respectively. According to the results of the maximum entropy model, annual precipitation factors, lithology, distance to road and drainage land use are important in landslide occurrence, respectively. According to landslide susceptibility zonation maps in both models, more than 50% of landslides occurred in high and very high susceptibility categories. Finally, the validation results of the models showed that the Demester shafer model with AUC-ROC index of 0.95 and classification accuracy with higher FR & SCAI index, greater efficiency and desirability for zoning, modeling and landslide prediction in the study area.
azadeh bazrmanesh; Mostafa Tarkesh; Hossein Bashari; Saeid Poormanafi
Abstract
In order to model the potential habitat of Bromus tomentellus Boiss and study the effect of climate change on the habitat of this species in Isfahan province method of modeling maximum entropy (MAXENT) were used. The species event data were determined by random categorization method using field visits ...
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In order to model the potential habitat of Bromus tomentellus Boiss and study the effect of climate change on the habitat of this species in Isfahan province method of modeling maximum entropy (MAXENT) were used. The species event data were determined by random categorization method using field visits and geographic information system including 60 rangeland locations as educational points. Also, 20 points of occurrence were surveyed using GPS in the western region of Isfahan as points of assessment. 22 environmental layers including 3 physiographic variables and 19 climate variables derived from temperature and rainfall were used in the modeling process. Using by Maxent, the relationship between species incidence and environmental factors was determined. Then, the effect of climate change using cluster variables of CCSM4 general circulation model was evaluated under two scenarios RCP2.6 (optimistic) and RCP8.5 (pessimistic) on geographic distribution of Br.tomentellus Boiss. Regarding the photo curves, the specie’s behavior relative to the environmental variables of Br.tomentellus Boiss in the range of 2500 to 3500 altitudes, slope 10 to 30 degrees, annual precipitation is 240 to 260 mm and the average temperature is 8 to 10 ° C are more likely to occur. The habitat of the species studied during the two periods of 2050 and 2070, it was observed that under the optimistic scenario, 46.1 square kilometers to the appropriate level of the habitat of the Br.tomentellus Boiss is increased and under a pessimistic scenario, about 35.74 km2 is reduced from the appropriate level of habitat of this species.
Mohammad Ali Zare Chahouki; mahbobe abbasi
Abstract
Rheum ribes species is one of the important medicinal plants in the world. In this study were used maximum entropy method (Maxent) and the MAXENT software to this prediction habitat map. Measure environmental variables was soil variables including gravel percentage, pH, electrical conductivity, percent ...
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Rheum ribes species is one of the important medicinal plants in the world. In this study were used maximum entropy method (Maxent) and the MAXENT software to this prediction habitat map. Measure environmental variables was soil variables including gravel percentage, pH, electrical conductivity, percent lime, gypsum, organic matter, soluble salts (Ca+, Na+, K+, Mg2+, CL, HCO3, SM and SO2), sand, clay and silt and variable topography (slope, aspect and elevation) and rainfall variable. Those were effective variables on the presence of species. The model classification accuracy using the area under the curve (AUC) was 95% (good Level), and kappa coefficient was obtained 0.92 that measuring from the agreement of prediction maps with ground truth, which is at a high level. The results of this study showed that the habitat of this species is in the soils with low pH (less than 8), clay Low (less than 40%), coarse texture and organic matter more than 4.0 percent. And the presence of this species has inverse relationship with a pH of both the depth and the clay first depth and with has directly relationship to organic matter of both depths.
