فهرست مطالب k. shirani

Groundwater is always considered one of the important water resources, especially in arid and semi-arid regions of the world, such as Iran. In recent decades, it has decreased drastically due to excessive use. The objective of this study was to determine the best interpolation method and evaluation of the spatiotemporal variations for the groundwater level in the Sahneh-Biston plain of Kermanshah province during three decades from 1991 to 2020. At first, four Gaussian, linear, spherical, and power semi-variograms were obtained for observations. Then, the best semi-variogram and interpolation methods were selected among the evaluated methods for zoning the groundwater level in the region. The lowest value of the sum of RMSE, MBE, and MAE error criteria and the highest coefficient of determination (R2) between observations and estimates in all three decades and the average of the entire period were calculated and considered to evaluate the most appropriate semi-variogram and interpolation methods for spatial distribution. The results showed that the ordinary kriging method with Gaussian semi-variogram is the best method to estimate the groundwater level in the Sahneh-Biston plain. The average difference between the minimum and maximum groundwater levels based on the observation wells of the study area and the zonation method is from 1279 to 1372 meters and 1289 to 1409 meters during the studied period time, respectively. The groundwater level is placed in more depth with the proximity to the central and southern regions. The maximum decrease and increase of groundwater level variations have been 12 and 19 meters during three decades, respectively. Also, the underground water level variations during these three decades showed that both the second and third decades compared to the first decade and the third decade compared to the second decade have increased in more than 50% of the region. This increase can be caused by the optimum management and water use in these years. Therefore, groundwater level monitoring provides effective help for experts and users in planning and optimal management of groundwater for the sustainable development of water resources.

Nosocomial infections are one of the important difficulties of medicine that cause infectious diseases, prolongation of hospitalization, financial and spiritual costs, and finally, mortalities. Nurses have an important role in preventing infections. Having appropriate knowledge is essential for proper performance also is the requirement of the right attitude. This study aimed to evaluate nurses' knowledge, attitude, and practice regarding preventing nosocomial infections in intensive care units.

This descriptive cross-sectional study was conducted ‎on nurses of the intensive care unit of Alzahra ‎Hospital in Isfahan in 2018. Seventy-seven nurses ‎with at least six months of work experience were ‎selected by the viable sampling method. ‎The data was collected by a four-part questionnaire ‎including demographic characteristics, knowledge, ‎attitude, and practice.‎ Data were analyzed by SPSS 21 software using the Pearson correlation coefficient.

71.4% of the nursing staff had average knowledge, 58.4% had moderate attitudes toward preventing nosocomial infections, and 54.4% had an intermediate level of practice. There was no significant correlation between knowledge and attitude; there was no correlation between attitude and practice, but there was a significant relationship between knowledge and practice (p<0.05).

Because most ICU nurses have moderate knowledge, attitude, and practice toward preventing nosocomial infections, reeducation seems to be necessary.

Delineation of gully erosion susceptible areas by using statistical models, as well as optimum usage of existing data and information with the least time and cost and more precision, is important. The main objective of this study is to determine the areas accuracy to gully erosion and susceptibility mapping by using data mining of the bivariate Dempster-Shafer, linear multivariate statistical methods and their integration in Semirom watershed, southern Isfahan province. First, the geographical location of a total of 156 randomly gullies were mapped using preliminary reports, satellite imagery interpretation and field survey. In the next step, 14 conditioning parameters of the gullies in the study area were selected including the topographic, geomorphometric, environmental, and hydrologic parameters using the regional environmental characteristics and the multicollinearity test for modeling. Then, the Dempster-Shafer statistical, linear regression, and ensembled methods were developed using 70% of the identified gullies and 14 effective parameters as dependent and independent variables, respectively. The remaining 30% of the gully distribution dataset were used for validation. The results of the multivariate regression model showed that land use, slope and distance to drainage network parameters have the most significant relation to gully occurrence. The gully erosion susceptibility maps were prepared by individual and ensemble methods and they were divided to 5 classes of very low to very high rate. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate gully erosion susceptibly maps. The verification results showed that the AUC of ensemble method (0.948) is higher than Dempster-Shafer (0.924) and Multivariate regression (0.864) methods. Also, the the seed core area index (SCAI) value of the ensembled model from very low to very high susceptible classes have a decreasing trend that indicating a proper separation of susceptible classes by this model.

Land subsidence as a hydrogeomorphology event is currently occurring dangerously in many plains of the country due to uncontrolled groundwater extraction from water bearing layers, and accordingly monitoring and studying this phenomenon seems to be necessary. In this study, land subsidence rate of the Najafabad aquifer was determined through the Differential Radar Interferometry (DInSAR) processing of the ASAR and PALSAR radar data and the results were validated by comparying with the differential leveling and groundwater level drowdown data. Processing of the ASAR sensor data estimates the land subsidence in the Najafabad plain at an average annual subsidence rate of 6.7 cm and a total of 41 cm during 6 years period and processing of the PALSAR data suggests an annual rate of 7.7 cm and total subsidence of 30 cm during 4 years period. Most of the occured displacements are related to the Tiranchi, Koushk, Ghahderijan, Goldasht and Falavarjan cities. The simultaneous groundwater level data with acquisition date of the radar satellite images between 2002 and 2014 shows a drawdown ranging from 0.5 to 46.5 meters in the south and east to north of the Najafabad city consistent with the estimated land subsidence areas.The DInSAR processing of the PALSAR data has led to a more accurate results with higher spatial resolution. Results of the radar data processing can be employed for the hazard zonation directly utilized for management and planning of control and preventive measures.

Selection of the appropriate distribution function and estimation of its parameters are two fundamental steps in the accurate estimation of flood magnitude. This study relied on the concept of optimization by meta heuristic algorithms to improve the results obtained from the conventional methods of parameter estimation, such as maximum likelihood (ML), moments (MOM) and probability weighted moments (PWM) methods. More specifically, this study aimed to improve flood frequency analysis using the Artificial Bee Colony algorithm (ABC). The overall performance of this algorithm was compared to the conventional methods by employing goodness of fit statistics, correlation coefficient (CC), coefficient of efficiency (CE) and root mean square error (RMSE). The study area, Babolrood catchment located in southern bank of Caspian Sea, has been subjected to annual flooding events. A total of 6 hydrometry stations in the study area were delineated and their data were used in the analysis of 6 distribution functions of Normal, Gumbel, Gamma, Pearson Type 3, General Extreme Value and General Logistic. This analysis indicated that Gamma and Pearson Type 3 were the most appropriate distribution functions for flood appraisal in the study area, according to the ABC and conventional methods, respectively. Also, the results showed that ABC outperformed ML, MOM and PWM; so, Gamma could be recommended as the most reliable distribution function for flood frequency analysis in the study area.

Water resources management depends on the precise assessment of water storage and access in each region, as well as environmental interactions of these resources. The man objective of this study was to delineate the potential zones of groundwater storage using FAHP. Mapping and assessment of it required maps of geomorphology, drainage, density, lineament density, slope and vegetation, which were initially prepared as the input layers in FAHP; the appropriate weights were attributed to them based on FAHP. Potential zones of ground water were classified into five classes of poor, average, good, very good and excellent. The number and density of available wells and springs in the study area dealt with the potential of the region for groundwater storage. So, ROC was used to assess the validation of results, considering spring points as signs of water resources. According to the results, classes of very good, good, average, weak, and very weak were ranked as the first to the last in terms of privilege order with an area of 37.7, 55, 40, 107, and 98.4 square kilometers, respectively.

Landslide is one of the natural phenomena that often rapid fall of the volume of sediments occur along domains. One of the undeniable effects of this phenomenon, intensification of soil erosion and transmission sediment to behind the dams of drainage basins. Since that the predicted time of occurrence of landslide is out of human knowledge, therefore, identification of sensitive areas to landslides and rating it can help prevent some of the risk of landslide occurrence. One of the main measures in this regard are mapping landslide hazard event. This process that based on understanding the natural features and quantitative modeling based on data from the study area occurs, can be a basis for further actions and planning for future development in the regional scale, regional and local level to be considered. Many researchers have studied the landslide zoning, Including: Gee, 1992, Van Westen et al, 1997, Guzzetti, et al, 2000, Saro Lee et al, 2001, Murat et al, 2002, Chowd Hury et al, 2003, pozhm, 1996, hagh shenas, 1996, bodaghi, 1997, farahani, 2001, sefidgari, 2002, shirani, 2004, izadi, 2006. 

Marbor river has a high potential for landslides and large landslides that have occurred. For mapping landslides in this area, aerial photo 1:40000, 1993 was interpreted and slip areas were identified and their positions in Google earth were determined. And finally position of 113 landslides in this area in GIS used. Several factors are involved in the occurrence of landslides, in this study, 9 factors lithology, land use, vegetation, rainfall, slope, aspect, distance from roads, distance from faults and distance from rivers was investigated and a total of 54 parameters were obtained for landslide zoning. 2.1 Landslide Index Landslide index is percentage slip level in each zone to zone an area divided by the total surface area of proportion to the total slip. (Van Westen 1998, shirani, 2005). Equation1: Li=((Si/Ai)/(∑In(Si/Ai))*100 That: Li is index of risk landslide occurrence in each area to percent, Si is area of landslide in each danger zone, Ai is area of each danger zone, n is number of zones. 2.2Accurately model (P) The accuracy of the method is sliding surface area ratio in the medium to high risk zones to total area of the zone. (farahani, 2001, shirani et al, 2010). Equation 2: P = KS/S That, P is model accuracy in medium to high risk zones. KS is area of landsliding in the medium to high risk zones. S is area of danger zones. 2.3 Accuracy of model (Qs) to determine the accuracy of the model, first Density ratio (Dr) is calculated and then accuracy of model by equation 4 is calculated. (Gee, 1992, shirani, 2005). Equation 3: Dr= Percentage of landslides/percentage of area Equation 4: That: Qs is total quality. Dr is Density ratio. N is number of risk categories. S is area ratio to the total area of each category of risk. 3-discussion Results showed that, AHP expertise in low sensitivity zones with the highest percentage of land area is allocated to the region and the area has the lowest percentage of area at risk is very high and the highest percentage of moderate-risk zone is located on the sliding surface. Qs of model was 0/35 and P model was 1/53, but increasing the compression ratio and landslide susceptibility index model in this region is moderately messy and in AHP consolidated method range with low sensitivity to most of the area is allocated and range with very low risk, the region has the lowest surface area and surface slip, but most of the slip surface located in high-risk area. 

This study was conducted with AHP expertise and AHP consolidated to landslide hazard zonation. For comparison logical models all factors and parameters were identical. Finally, 9 factors in 54 parameters as risk factors for the zoning district were selected and entered to the regression equation. Factors and parameters set using experts and field observations for AHP expertise were prioritized and entered to the process. Also, from the regression coefficients obtained and comments expertise integrated was used for AHP consolidated. After processes zoning models produced were put to the test. In AHP consolidated method factors such as distance from roads, lithology, vegetation, distance from river, land use, slope, aspect, rainfall and distance from fault had the greatest impact respectively. According to the results, model compilation for all zones of the AHP consolidated method, compliance with conditions of more landslides. According to the chart of landslides, AHP consolidated method in introducing the zones with very high sensitivity most important is the capability. In AHP consolidated method, values of P and Qs 1/73 and 0/45 respectively, in AHP expertise method 1/57 and 0/35. The numerical values of these parameters is more, models of higher accuracy is obtained. In fact P showed map accuracy of medium and high to top risk categories and the higher the value of these parameters, the model is closer to reality and any amount is more Qs map accuracy is higher for all categories and finally, the validity of the model used is higher. Therefore in this study, AHP consolidated method to the accuracy of indicators (Q) and total Quality (Qs) have better performance than the AHP expertise method. Weighting consolidated method, have more power than the expertise method for prioritization of appropriate parameters.