Showing 6 results for Ekhtesasi
H. Azimzadeh, F. Fotoohi, M. R. Ekhtesasi,
Volume 18, Issue 68 (summer 2014)
Abstract
Soil surface roughness (SSR) is one of the important factors in wind and water erosion studies and control. Several parameters such as surface rock fragments influence SSR. Main objectives of this paper are to study and compare (Allmarass) random roughness (RR) and tortuosity (Tb) indices in coarse, medium and fine grain plains of Yazd-Ardakan and investigate the relationship between indices and desert pavement coverage. Roughness data were obtained by pin roghness-meter and roller chain in the three mentioned plains. RR and Tb were measured in 90cm transect length with systematic sampling pattern after determining the boundary of three kinds of plains. In each plain, 30 transects were randomly sampled and the height of soil surface roughness fractions was recorded. Distance of pins in the applied roughness meter is about 2cm. Therefore, in each 90 cm transect the height of 46 points was measured. Desert pavement coverage was measured in 20×20cm2 plots. The result showed that desert pavement coverage in coarse, medium and fine grain plains were in the range of 55-100, 40-85 and less than 5%, respectively. The relationship between RR and desert pavement was significant. The result of ANOVA (Duncan) showed, RR and Tb were significantly different in coarse, medium and fine grain plains (p-value<0.01). In addition, by increasing desert pavement percentage RR and Tb increased exponentially in base of Neper number. The relation between RR and desert pavement coverage is stronger than Tb and desert pavement coverage. Correlation between the two measured indices was calculated and compared in different plains. The result revealed that about 54, 33 and 14% of the arability in Tb could be explained by RR in coarse and medium grain plains, respectively. The correlations of two indices were significant in coarse and medium plain and insignificant in fine grain plain. The trend of RR and Tb decreased from mountain to plain center. Although RR increased slightly in fine grain plain, the difference was not significant.
M. Hayatzadeh, M. R. Ekhtesasi, H. Malekinezhad, A. Fathzadeh, H. R. Azimzadeh,
Volume 21, Issue 1 (Spring 2017)
Abstract
Soil erosion is undoubtedly one of the most important problems in natural areas of Iran and has destructive effects on different ecosystems. Considering that calculation of the sediment rate in sediment stations and direct measurements of erosion process is costly and difficult, it is critical to find ways to accurately estimate the amount of sediment yield in catchments especially in arid and hyper arid areas because of their high ecological sensitivity. One of the most commonly used methods in these areas is the sediment rating regression method. Therefore, in this study sediment observed data for 48 events (the corresponding discharge and sediment) in a 23-year period from Fkhrabad basin (Mehriz) were compared to the estimated data obtained from Multi-line rating method, extent middle class, middle class rating curve with correction factor QMLE, SMEARING correction coefficient FAO and Artificial Neural networks (ANNs). Finally, the accuracy of these methods were assessed using different evaluation criteria such as Root Mean Square Error (RMSE), coefficient of determination (R2) and the standard Nash (ME). Results showed that ANN outperformed the other methods with the RMSE, R2 and ME of 203.3, 0.86 and 0.66, respectively. The results suggest that these methods should be used cautiously in estimating the suspended sediment load in arid and hyper arid regions due to the nature of the observed data and temporal and seasonal flow systems in these regions. It was also indicated that the artificial neural network models have higher flexibility than other methods which makes them to be useful tools for modeling in poor data conditions.
J. Chezgi, H. Maleki Nezhad, M. R. Ekhtesasi, M. Nakhaei,
Volume 22, Issue 1 (Spring 2018)
Abstract
Underground dams are structures built in underground and are capable of saving and making the underground water available. In this research, by using the SWOT analysis model, suitable locations were investigated for the development of an underground dam in the Keriyan area of Hormozgan province. At first, the necessary data and information were provided by visiting the region and presenting a questionnaire to the residents of the area and experts to investigate the strengths, weaknesses, opportunities and threats in the region for the underground dams. In the final step, by using the SWOT model and QSPM matrix, a comprehensive and appropriate strategy for underground dams was determined. The results showed that among the internal factors, not decreasing the volume of the reservoir due to deposits and reducing the evaporation from the reservoir with a final value of 0.85 and 0.66, and among the external factors, the willingness and cooperation of the relevant organizations and the disruption of downstream water rights with a final value of 0.68 and 0.66 had the greatest impact on selecting the strategy. Based on the results related to the internal and external factors, the strategy was placed in the maximum-maximum quadrant; in line with the strategy, by using the strengths and opportunities, the weaknesses should be overcome and the threats should be tackled. Some strategies were presented. In order to prioritize these strategies, the quantitative matrix QSPM was used. Finally, the hydrological, economic, social and environmental evaluating strategies of underground dams, before and after the construction, with a final score of 19.3 were prioritized.
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.
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.
E. Soheili, H. Malekinezhad, M. R. Ekhtesasi,
Volume 23, Issue 4 (Special Issue of Flood and Soil Erosion, Winter 2019)
Abstract
The Kor River in Fars province supplies an important part of water requirement in the Doroodzan dam basin and its surrounding area. In this study, the meteorological and hydrological droughts of this area were investigated in the last four decades. For this purpose, the temporal and spatial trend variability of the stream flow was investigated in monthly, seasonal, and annual time scales in the 6 selected stations. The trends of Standardized Precipitation Index SPI, as the drought index, in the 5 selected stations were also studied by the modified Mann-Kendall method. The results indicated that the trend in the stream flow was decreasing in all time scales. Significant downward trends were observed at 95% confidence level on monthly, annual and monthly time scales, especially in the warm months from May to September. These significant downward trends were located spatially in the stations located near the agriculture area, in the middle part of the basin. The significant upward trend existed only at the Doroodzan dam station, at the outlet in the area and in the warm months of the year. In the case of the SPI index, trends were decreasing in all time scales and were significant only at 2 stations in the long-term periods, 9, 12, and 18 months, at 95% confidence level. The results, therefore, indicated the occurrence of severe droughts (SPI<-2) during 1982-83 and 2007-8 periods.
