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Showing 4 results for Jahanbakhshi
Comparison Effect of Phytoremediation in Cadmium and Chromium Contaminated Soil in Spinacia oleracea and Lepidium sativum
S. Jahanbakhshi, M. R. Rezaei, M. H. Sayyari-Zahan,
Volume 18, Issue 70 (winter 2015)
Abstract

Phytoremediation is one of the cleanup methods of polluted soil that is possible accumulation of heavy metals in plant tissues, exclusion of these elements from contaminated soil. Therefore, to achievement the objective, this research was done in pot culture using completely randomized design at the University of Birjand in 2011. Two species Spinacia oleracea and Lepidium sativum were used to remove or reduce the concentration of Cadmium (Cd) and Chromium (Cr). In this study, different levels of Cadmium (CdCl2) concentrations including 5, 50, 100 mg kg-1 and also chromium (CrCl3) concentrations 50, 100, 150 mg kg-1 were used respectively and control as well for each species with three replications. Results indicated that the Cd and Cr concentration in shoot of Spinacia oleracea and Lepidium sativum significantly affected by their concentration in soil (p<0/01). Results revealed that increasing of Cd and Cr concentrations in soil, showed an increase concentration of both metal in shoot of Spinacia oleracea. increasing of Cd concentrations in soil, showed an increase concentration of it in shoot of Lepidium sativum but the concentration of Cr was less. Also, comparison of cadmium and chromium concentrations in shoot of Spinacia oleracea and Lepidium sativum showed that two species showed same behavior of Cd and different behavior Cr concentration. So the analysis of data showed that both of species are appropriate for absorption of Cd and Cr and phytoremediation technology as well. It can be concluded that in high soil Cr concentration for phytoremediation Lepidium sativum is not appropriate.


Investigating Suitable Areas for Flood Spreading Using Fuzzy Logic and Analytic Network Process (ANP) (Case Study: Mashhad Plain)
E. Zahedi, F. Jahanbakhshi, A. Talebi,
Volume 20, Issue 77 (Fall 2016)
Abstract

In this research, to locate and prioritize suitable areas for flood spreading in Mashhad plain, 10 criteria were used including land use, slope, alluvium thickness, distance to well, distance to subterranean, distance from the village, water table drawdown, permeability coefficient, electrical conductivity, and drainage density. Weighting process was done by Analytic Network Process (ANP) and fuzzy logic. After preparing and weighting the maps of all appropriate measures for locating suitable areas of flood spreading maps based on fuzzy logic and analytic network process model, the final map was prepared for prioritizing suitable areas for flood spreading. Then by applying the limiting layer that is a combination of three criteria of land use, slope and geomorphology, the final map of suitable areas for flood spreading was prepared and prioritized. The results showed that among the 10 factors influencing flood spreading, the thickness of alluvium criteria by weight of 0.27 was identified as the most effective layer in suitable areas for flood spreading. Most of the suitable regions located in slope less than 3% that represents its considerable impact in implementation of flood spreading. Mashhad plain potential for flood spreading, after removing exception areas (40.8% of total area), were defined in four inappropriate, relatively appropriate, appropriate and perfectly appropriate classes, that include 2.7, 25.9, 26.5 and 1.5% of the plain area, respectively.


Investigation of Sediment Production and Runoff Generation on Rock Formations of Shirkooh Slopes of Yazd by Using a Rainfall Simulator
F. Jahanbakhshi, M. R. Ekhtesasi, A. Talebi, M. Piri,
Volume 22, Issue 2 (Summer 2018)
Abstract
One of the main sources of runoff in arid and semi-arid mountainous highlands is typically composed of before Quaternary formations. Since the structure and lithology of formations are different, varying formations can have different significance in terms of runoff and sediment. The present study aimed to investigate the sediment production potential and the runoff generation threshold on three formations (Shirkooh Granite, Shale, Sandstone and Conglomerate of Sangestan and Taft Limestone) in Shirkooh mountain slopes. The 60 mm/h rainfall intensity with the 40 minute continuity, according to region rainfall records, and the ability of the rainfall simulator were selected as the basis for the study. Field experiments were conducted in dry conditions based on one square meter plot on rocky slopes with a gradient of 20 to 22 percent and a maximum thickness of 30 cm of soil. The results showed that in 60 mm/h rainfall intensity, the minimum rainfall to produce runoff on Sangestan, Shirkooh and, Taft, was 10, 10.7 and 16.7 mm, respectively. The maximum amount of the sediment was measured on Sangestan, Taft and Shirkooh, respectively. Statistical tests related to runoff and sediment production on all three formations confirmed a significant difference at the 5 % level. In terms of the time required to start runoff, the minimum time was for Sangestan, Shirkooh and Taft, respectively. According to the results, in terms of the potential for runoff generation and sediment production, Sangestan, Shirkooh and Taft can be ranked from high to low levels.

Performance Evaluation of Three Image Classification Methods (Random Forest, Support Vector Machine and the Maximum Likelihood) in Land Use Mapping
F. Jahanbakhshi, M. R. Ekhtesasi,
Volume 22, Issue 4 (Winter 2019)
Abstract
Land use/cover maps are the basic inputs for most of the environmental simulation models; hence, the accuracy of the maps derived from the classification of the satellite images reduces the uncertainty in modeling. The aim of this study was to assess the accuracy of the maps produced by machine learning based on classification methods (Random Forest and Support Vector Machine) and to compare them with a common classification method (Maximum Likelihood). For this purpose, the image of the OLI sensor of Landsat 8 for the study area (Sattarkhan Dam’s basin in the Eastern Azerbaijan) was used after the initial corrections. Five land uses including urban, irrigated and rain-fed agriculture, range and water body were considered. For conducting the supervised classification, ground truth data were used in two sets of educational (70% of the total) and test (30%) data. Accuracy indexes were used and the McNemar test was employed to show the significant statistical difference between the performances of the methods. The results indicates that the overall accuracy of Support Vector Machine, Random Forest, and Maximum Likelihood methods was 96.6, 90.8, and 90.8 %, respectively; also the Kappa coefficient for these methods was 0.93, 0.81 and 0.83, respectively. The existence of a significant statistical difference at the 95% confidence between the performances of the Support Vector Machine algorithm and the other two algorithms was confirmed by the McNemar test.

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