Showing 8 results for Pourghasemi
S.h Sadeghi, S.h Pourghasemi, M Mohamadi, H Agharazi,
Volume 12, Issue 46 (fall 2009)
Abstract
The use of suitable empirical models for estimation of soil erosion and sediment yield is essential because of nonexistence or shortage of associated data in many watersheds. In the present study, the applicability of the USLE and its different versions Viz. MUSLE-S, AOF, MUSLT, MUSLE-E, USLE-M and AUSLE in estimation of storm-wise sediment yield from standard plots installed in dry farming, ploughed and rangeland treatments was evaluated. To conduct the study, the entire input data were collected from plots installed in three replicates in each treatment in Khosbijan Natural Resources Research Station in Arak Township. The models’ estimates were then compared with the observed sediment data for 12 storm events. Contrary to high correlation among different models’ estimates, the models used in estimation of measured sediment data were found inapplicable. However, significant relationship (r=94.4%) and non-significant relationship with correlation coefficients less than 50% were found between MUSLE-E, and MUSLE-S and MUSLE-E estimates and measured data in rangeland, dry farming and ploughed treatments, respectively.
H Pourghasemi, H Moradi, M Mohammadi, M Mahdavifar,
Volume 12, Issue 46 (fall 2009)
Abstract
One of our first activities in natural resources management and development programs is to acquire knowledge on Landslide Susceptible areas. The aim of this research is landslide hazard zonation in some part of Haraz watershed between Vana village and Emam zadeh Ali, using fuzzy membership functions and fuzzy operators. At first landslide points were recognized using arial photography and field studies. Afterwards, the inventory map of landslide was prepared. Then, each effective element in landslide such as: slope, aspect, elevation, lithology, landuse, distance of road, distance of drainage, distance of fault and precipitation map was prepared in GIS environment.These data were saved in raster and vector format in ILWIS software and used for analysis with theory of fuzzy sets. Fuzzy analysis was made by IDRISI software, after assigning value and fuzzy membership functions. In this research we used different fuzzy operators such as (And, Or, Sum, Product and Gamma). Results showed Gamma fuzzy operator had the best accuracy ( ) in making landslide susceptibility map in study area.
M Gorji, H Eshghizadeh, A Khosh Goftarmanesh, A Ashrafi, A Moalem, N Poursakhi, N Pourghasemian, A Miladi,
Volume 12, Issue 46 (1-2009)
Abstract
Iron deficiency is a worldwide nutritional constraint in agricultural lands especially in calcareous soils. Cultivation of crops tolerant to Fe-deficiency is an approach to combat Fe deficiency. The aim of this investigation was to evaluate Fe-efficiency of selected important crops in Iran. A completely randomized block design in triplicates was conducted at IUT research greenhouse in fall 2006. Sweet corn (Hybrid K.S.C. 404), grain corn (Hybrid S.C. 500), safflower (cvs. S3110, S-411), sunflower (Hybrid Hyson) and durum wheat (cv. Shuga) were grown in a nutrient solution at two Fe levels (1 and 10 µM Fe-EDTA). The results showed significant (P < 0.01) variation among the studied crops in Fe-efficiency. Corn hybrids were more sensitive to Fe deficiency (FeE = 26%) as compared to other studied crops, and the greatest reduction was observed in their shoot dry matter at 0.1 mM Fe- EDTA treatment. In contrast, the lowest decrease in root and shoot dry matter weight under Fe-deficient condition was found for durum wheat (FeE=94%). Comparing the calculated Fe-efficiency using different indices showed that Fe concentration and content in the whole plant, shoot and root had no relationship with crop tolerance to Fe deficiency.
N Pourghasemin, M Zahedi,
Volume 13, Issue 47 (4-2009)
Abstract
This experiment was conducted at the Agricultural Research Station of Isfahan University of Technolgy in 2006 to evaluate the effects of planting pattern and the level of soil moisture on two safflower cultivars. A factorial split plot arrangement was used in a randomized complete block design with three replications. Two planting patternS (flat and bed planting) and two levels of soil moisture (irrigation after 80 and 100 mm cumulative evaporation from Class A pan) were considered as the main factor and two safflower cultivars (IL 111 and Kosseh) as minor factor. Each plot in flat planting consistedof six rows, spaced 25 cm apart with plants 8 cm apart and in bed planting consisted of four rows, spaced 45cm apart with plants 5 cm apart. The duration from planting to button formation, 50% flowering, and 100% flowering stage were significantly shorter in 45cm bed planting than in 25cm flat planting. The duration from planting to all growth stages was less in IL 111, compared to Kosseh cultivar. The level of soil moisture did not affect the duration of any growth stages. Plant height, leaf area index, plant dry matter, number of buttons per plant, number of grains per button, grain weight, and harvest index were higher in flat planting, compared to bed planting. Plant height, plant dry matter, number of buttons per plant, number of grains per button, grain weight, and harvest index were reduced as the level of soil moisture was decreased. Leaf area index and plant dry matter were not significantly affected by the level of soil moisture at 50% flowering stage. Regardless of the level of soil moisture and cultivar, the grain yield was 36% more in flat planting than bed planting. The grain yield was more at higher level of soil moisture and also in Kosseh than in IL 111. The oil percentage and oil yield was higher in flat planting, compared to bed planting and also in Kosseh than in IL 111. The oil percentage was not significantly affected by the level of soil moisture. However, the oil yield was decreased as the level of soil moisture was reduced. The highest amount (1168 kg/ ha) of oil yield was obtained from Kosseh in flat planting and the lowest amount (417 kg/ ha) was achieved from IL 111 in bed planting. The results from this experiment show that to obtain the optimum yield from summer planting in areas with similar conditions to that of this study the 25cm flat planting compared to the 45cm bed planting, and Kosseh compared to IL 111 cultivar seems to be superior.
H. R. Pourghasemi, H. R. Moradi, M. Mohammdi, R. Mostafazadeh, A. Goli Jirandeh,
Volume 16, Issue 62 (Winte - 2013 2013)
Abstract
The aim of present research is landslide hazard zoning using Bayesian theory in a part of Golestan province. For this purpose, landslides inventory map was created by landslide locations of landslide database (392 landslide locations). Then, the maps of effective parameters in landslide such as slope degree, aspect, altitude, slope curvature, geology, land use, distance of drainage, distance of road, distance of fault, stream power index (SPI), sediment transport index (STI), and rainfall were prepared in GIS environment. Relationship between effective factors and landslide locations were considered using Bayesian probability theory. In the next step, parameters classes weights were found and the landslide susceptibility mapping was achieved by fourteen modeling approaches (using whole parameters and deleting parameters one by one). The verification results by ROC curve and 30% landslide locations showed that the Bayesian probability model has 71.37% accuracy for the second approach of modeling in the study area.
H.r. Pourghasemi, H.r. Moradi, S.m. Fatemi Aghda,
Volume 18, Issue 70 (winter 2015)
Abstract
The objective of the current research was to prioritize effective factors in landslide occurrence and its susceptibility zonation using Shannon’s entropy index in North of Tehran metropolitan. To this end, 528 landslide locations were identified using satellite images such as Geoeye (2011-2012), SPOT-5 (2010), and field surveys, and then landslide inventory map was created for the study area in ArcGIS environment. Data layers such as slope degree, slope aspect, plan curvature, altitude, lithology, land use, distance of road, distance of fault, distance of drainage, drainage density, road density, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), surface area ratio (SAR) and topographic position index (TPI) were created and the mentioned maps were digitized in GIS environment. Prioritization of effective factors by Shannon’s entropy index showed that the layers such as land use, lithology, slope degree, stream power index, and NDVI had the most effect on landslide occurrence. However, factors of topographic position index and plan curvature had the least effect. Also, landslide susceptibility zoning by the mentioned model and its accuracy assessment using relative operating characteristics (ROC) curve and 30 percent of landslide locations showed an accuracy of 82.83% with a standard error of 0.0233 in the study area.
S. Ayoubi Ayoublu, M. Vafakhah, H.r. Pourghasemi,
Volume 26, Issue 3 (Fall 2022)
Abstract
Population growth, urbanization, and land use change have increased disastrous floods. Iran is also among the countries at high risk of floods. The latest examples of flood damage are the devastating floods of the spring of 2019 with significant mortality and financial losses in more than ten provinces of the country. The purpose of this study is to prepare an urban flood risk map of District 4 City Shiraz. The vulnerability of the region was made using PROMETHEE Ⅱ and COPRAS multi-criteria decision-making models and urban flood hazard zones were prepared by partial least squares regression (PLSR) and ridge regression (RR) models and a risk map was obtained by multiplying the vulnerability and hazard in ArcGIS software. The highest percentage of the study area in the PROMETHEE Ⅱ and COPRAS models belongs to the moderate class of vulnerability. The evaluation of the vulnerability models using Boolean logic and RMSE and MAPE statistics, showed that the COPRAS model provided better results than the PROMETHEE model. The results of partial least square regression (PLSR) and ridge regression (RR) models in flood risk modeling were analyzed by the Taylor diagram, which showed the superiority of the ridge regression (RR) model and the accuracy of this model in preparing urban flood hazard maps. The risk map of the study area indicated that 34% of the area (973 ha) is in the range of high and very high flood risk.
S. Yaghobi, Ch.b. Komaki, M. Hosseinalizadeh, A. Najafinejad, H.r. Pourghasemi, M. Faramarzi,
Volume 27, Issue 1 (Spring 2023)
Abstract
Frequency analysis of daily rainfall or return period of rainfall and flooding events is very important considering the behavioral complexity in water resources management; because ignoring it can lead to urban destructive floods. In the present research, three distribution functions of Pearson, Beta, and Gamma were compared to investigate and select the most appropriate distribution function for the precipitation data acquired from meteorology stations and CHIRPS satellite in seven stations in the watershed of Bustan Dam. Statistical analyses showed that satellite data were ineffective to estimate daily precipitation due to high errors in RMSE, MAD, and NASH. Meteorological data were used to spot the best distribution. Google Earth Engine and Python programming language were used. Then, the selected distribution function was used to determine the maximum daily rainfall, frequency probability, and return period of 2, 10, 50, 100, and 200 years. The results of the goodness of fit test, Error Sum of Squares, Bayesian Information Criterion, Akaike Information Criteria well as Kullback-Leibler Divergence showed that in five stations of Kalaleh, Qarnaq, Golestan National Park, Golestan Dam, and Glidagh, the Pearson function is the most suitable distribution function. Also, in the other two stations (Gonbad and Tamar), the Beta function was recognized as a suitable function. However, Gamma distribution in the study area is not efficient. So, it can be concluded that heavy and irregular rainfall can be effective in choosing the best distribution function at each station. Therefore, it is recommended to consider the maximum possible rainfall and as a result of the possible occurrence of floods with principled and accurate management to prevent human and financial losses in susceptible areas, especially in the study area.
