جستجوی مقالات مرتبط با کلیدواژه « مدل » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

The cycle of soil erosion (including removal, transport and deposition) that controls the sedimentation of watersheds, includes a set of complex and highly nonlinear processes. On the other hand, the factors influencing sedimentation in watersheds are very diverse, and according to the specific conditions of climate, soil, vegetation, geology, topography, etc., in each basin, the weight and role of each of the mentioned factors in sediment production is very different. Accurately determining and measuring these factors and making mathematical relationships between them are often difficult, expensive, time-consuming and error-prone, and this is the case with the use of models based on computational intelligence and the use of a limited number of basin dynamic variables, it is possible to simulate the behavior of the watershed in sediment production. Regardless of the type of intelligent models, in most of the conducted research (especially in internal research), the simulation of suspended sediment is mainly based on the discharge variable and the role of variables such as precipitation (especially precipitation obtained from satellite images), which are effective in the sedimentation of basins, have received less attention. In addition to precipitation, the skewness of sediment measurement data is also one of the issues that lack of recognition and attention will reduce the efficiency of estimator models. In the present study, the role of variable daily rainfall (taken from CHIRPS satellite) in the simulation of suspended sediment of Qarachai River has been investigated.

Multi-layer perceptron artificial neural network was used in order to simulate the daily suspended sediment concentration of Qarachai River (at the Ramian hydrometer station in Golestan province). In this regard, the variables of discharge and previous discarge (in instantaneous and daily scales) as well as the average daily and previous rainfall of the basin (taken from CHIRPS satellite) for a statistical period of 37 years (1980-2017) as variables model input was used. In order to increase the generalization power of the models, self-organized mapping neural network (for data clustering) and gamma test was used to find the best combination of input variables. In order to improve the efficiency of network training, a variety of activation and loss functions as well as the overfitting prevention algorithm were used. In order to investigate the effect of using activation and loss functions in suspended sediment estimation, different scenarios were considered, which led to the construction of 9 models. After that, using validation indicators, the effectiveness of the models in simulating suspended sediment was investigated and compared, and then the best model was selected.

The results obtained from the present research showed that among the different models, the neural network model with Huber's activation function and ReLU loss function, having the average absolute value of the error equal to 368 mg/l, the root mean square error equal to 597 mg per liter, the Nash-Sutcliffe coefficient of 0.87 and the percent bias -2.2% were selected as the best model. The results also showed that the use of the rainfall variable (as one of the important factors in causing erosion and sediment transfer in the basin) has improved the efficiency of the models, therefore, considering the ease of using CHIRPS satellite rainfall data, it is suggested in order to simulate the suspended sediment of rivers, this data is also used along with other predictive variables.

In the simulation of suspended sediment, discharge variable is often used as the only predicting variable of suspended sediment, while in basins with rainy, or rainy-snow regimes, the role of precipitation in the production of surface runoff and soil erosion is very important and plays an important role in the production and transport of sediment in the basin. In this regard, although the use of rainfall data obtained from ground rain gauge stations has played an effective role in increasing the efficiency of data-based models in estimating suspended sediment, however, the preparation of hundreds of spatial distribution layers of daily rainfall from the data point data of ground stations, the use of this variable in the simulation of the suspended sediment of the basin has been faced with many problems (such as the lack or inappropriateness of the spatial distribution of rain gauge stations, statistical deficiencies, the use of inappropriate interpolation methods and time-consuming calculations). Therefore, in practice, the variable of river flow is often used as a predictor of sediment, and precipitation is used less often. One of the solutions to the problem mentioned in the present study is the use of CHIRPS satellite data, which was investigated for the first time in this study. These data, available since 1981, can easily be used to simulate suspended sediment or other applications related to watersheds. Another important point that needs to be taken into account in the simulation of suspended sediment is the presence of high skewness in sediment measurement data (both suspended sediment and flow rate), which lack of attention in the process of training (or recalibration) and testing the models leads to It will lead to the construction of weak models in terms of efficiency and the existence of uncertainty in the accuracy of their results. In this regard, it is necessary to use logarithmic transformations or suitable functions of activation and loss in the training process, which in this research, two functions, ReLU and Huber, were proposed respectively. Another important point is to pay attention to the generalization power of data-based models, which is largely dependent on the data used in their calibration or training process. These data should be selected in such a way that while they are representative of the data in the entire statistical period, they are similar and have the same distribution with other data sets (such as cross-validation or test sets). According to the results obtained from the present research and in order to increase the efficiency of artificial neural network models in estimating the suspended sediment of watershed hydrometric stations, it is suggested to use the experiences obtained in this research in other sediment measuring stations of the country.

Addressing the issue of desertification due to the multi-criteria nature, increasing development, extensive and long-term, and the simultaneous impact of land resources and human populations is necessary to achieve sustainable development. On the other hand, despite the implementation of various plans to combat desertification in the country, due to the existence of numerous obstacles and interaction between these obstacles, these programs have not been able to make much progress in this area. Therefore, this study was conducted as a case study in Yazd province to analyze obstacles to combating desertification.

For this purpose, the interpretive structural modeling (ISM) method was used. In this method, effective obstacles were first identified through the study of theoretical foundations and the Delphi technique with the participation of 30 field experts. Then, using interpretive structural modeling (ISM) analysis, the relationships between obstacles were determined and analyzed in an integrated manner. Finally, using the MICMAC method, effective obstacles were analyzed according to the impact and effectiveness on other obstacles.

The results of the analysis showed that there are no obstacles in the link group that indicates strong influence and dependence. At the same time, it was observed that the obstacles of "inappropriate conversion and change of land use" (C31) with a driving force of 10 and "multiplicity of policy centers and lack of interaction between them" (C6) with a driving force of 9, have a highest influence. and three obstacles of " general ignorance about the destruction of environment, new methods and knowledge of day " (C1), " absence of comprehensive desertification control schemes " (C2) and "lack of specialized and trained manpower" (C11) are also jointly located in the stimulus or Influential area with the driving force of 8 and are among the main obstacles to achieving the goals of the implementation of combating desertification projects.

Therefore, paying attention to the analysis of effective obstacles among a wide range of obstacles to combat desertification can quickly improve the unfavorable conditions and create sustainable structural changes in the process of combat desertification. accordingly, it is suggested that the obtained results be taken into consideration in the plans of combating desertification to preventing the waste of national capital.

Specific climatic and geographical conditions of Yazd province have always exposed this region to a severe storm, and air pollution. Low visibility is the primary impact of air pollution due to atmospheric phenomena. Increasing dust density, along with a severe decrease in visibility, has harsh effects on the health of living things, socio-economic sections, and transportation systems. Therefore, identifying hazardous areas considering low visibility during dust events is important to manage and conserve the health of area residents. This study aims to evaluate the trend of changes in horizontal visibility of areas affected by dust storms in Yazd province, Iran. For this purpose, the quantitative model of horizontal visibility based on the relationship between Aerosol Optical Depth (AOD) of MODIS sensor derived from Deep Blue algorithm and horizontal visibility data derived from meteorological stations in the study area was introduced the horizontal visibility of dust events from 2015 to 2017 was mapped using geostatistics Kriging method. Results showed a significant relationship between aerosol optical depth and horizontal visibility data at the 99% level. The AOD value ranges from 0.1 in areas with no dust to 1.9 in areas with severe dust at six studied events, and the visibilities coincide with AOD values from 64 m in dust centers to 19951 m beyond these centers were estimated. Therefore, AOD image measures with more than 0.8 have horizontal visibility less than 1000 m. Determining of horizontal visibility extent without spatial limitation and identification of areas affected dust events are advantages of the quantitative model of horizontal visibility and its mapping. According to land cover/use map in Yazd province, mineral and constructing sites, high-density dirt roads, sand dunes, poor rangelands are the most effective factors in increasing of dust density and low horizontal visibility in pathways of dust storms of this province.

Feeding areas, and the spatial distribution of recharge in karstic aquifers, depends to factors such as geomorphology karst and its development, climate, slope, vegetation, soil and geological factors. Karstic aquifers in the study area, has the main role in the development of human civilization and supply water to communities. The purpose of this research is modeling the aquifer feeding areas Azhvan-Bisotun in Kermanshah province using KARSTLOP model. The results feed zoning show that the annual charge Bisotun karst aquifer obtained for between 36 to 83 percent. The greatest amount of recharge in areas with more than 78% charged, is accordance sinkholes and the recharge rate of 36 to 40% and 40% to 45% occur in alluvial plains. Bisotun geomorphology karst mountains represent a major role in the spatial distribution of values in the aquifer is recharged.

Electrical conductivity (EC) is an important factor in river engineering, especially studying of river water quality. In this study we studied and evaluated wavelet neural network to predict the electrical conductivity of the Kakareza river (in lorestan), and the results were compared with results of artificial neural network model. For this purpose, hydrogen carbonate, chloride, sulfate, calcium, magnesium, sodium and flow rate at monthly scale during the period (1969-2015) as input and output parameters were selected as electrical conductivity. The criteria of correlation coefficient, root mean square error and of Nash Sutcliff coefficient were used to evaluate and performance compare of models. The results showed that wavelet neural network model has the highest correlation coefficient (0.977), the lowest root mean square error (0.032 ds/m) and the highest standards Nash Sutcliffe (0.847) became a priority in the validation phase. The results indicate acceptable capability of wavelet neural network models to estimate the electrical conductivity of river water.

The amount of total dissolved solids (TDS) is an important factor in stream engineering, especially study of river water quality. This study estimates the TDS amount of Belkhviachayriver in Ardabil Province, using bayesian neural network-, gene smart and artificial neural network. Quality variables include hydrogen carbonate, chloride, sulfate, calcium, magnesium, sodium and inflow (Q) in monthly time scale during the period (1976-2009) as input and TDS were chosen as output parameters. The criteria of correlation coefficient, root mean square error and of Nash Sutcliff coefficientwere used to evaluate and performance compare ofmodels. The results showed that however the models could be used to estimate with reasonable accuracy the amount of dissolved solids in water deal, but regarding to accuracy, bayesian neural network model with the highest correlation (0.966), minimum root mean square error (0.094ppm) and the Nash Sutcliff (0.998) were put in the verification phase. The results showed that the bayesian neural network model to estimate high minimum and maximum values ​​of dissolved solids in water.

The forest roads supply accesses to various points in forest in order to timber transportation in forest plans, Silviculture operations, Training and other using of forest. Road construction made a surface without vegetation cover that lead to creation of erosion and sediment in forest area. In order to estimating of erosion and sediment in forest roads, a lot of models has been designed and introduced. This paper investigated of abilities and limitations of estimation model of forest roads erosion soil that has been designed. First the models have been divided in two physical and empirical, then Characteristics data and usable area for each models has been explained. The physical models were explained include WWEP, KINEROS2 and empirical model include ROADMOD¡ STJ-ERO, SEDMODL ¡SEDMODL2, WARSEM, FORECALT and CULSED. Eventually ability and limitation of each model was presented and discussed. Result of this paper showed that number of parameters required, temporal scale, operational area, estimation spatial and type of estimation (erosion and sediment) are the important Characteristics to judging about models. The overall conclusion of this research showed the most fundamental limitations this models include assembling total practicable data unusable for various regions.

In management, the monitoring means systematic collection and storage of data from activities and strategies that provide assessment and report about the overall condition of the study area. In this research, among different existing methods, IMDPA model was selected for monitoring desertification in Garmsar plain. Based on the study area two criteria including agriculture and water were selected as the main effective criteria on ​​desertification and desertification intensity was evaluated on the basis of seven indices including: groundwater table fluctuation, electrical conductivity, sodium absorption ratio, agricultural mention cropping pattern, agriculture yield according to land suitability, irrigation method and highly inappropriate utilization of machinery, fertilizer and pesticide chemicals. The results showed that the average weights of agriculture criterion were 2.17 and 2.27 in 2002 and 2011 respectively classified in medium class of desertification. Also, the index of irrigation method classified in very high class of desertification was the most effective factor on land degradation among studied indices in during 2002-2011. Studying the average weight of numerical value, it is distinguished that the intensity of desertification for the total area were 1.93 and 2.10 in 2002 and 2011 respectively.

Applications of rainfall-runoff models, are to determine the hydrologic behavior of watershed and to simulate the flow, for designing hydraulic structures and specially evaluating the impacts of operations on the flow regime. According to deficiency of hydrometer data in most of Iran’s watersheds, using simple and convenient hydrologic models and developing them for watersheds without stations is essential. In this context, several methods have been proposed. This study has been compared the consistency, accuracy and reliability of geomorphologic, geomorphoclimatic and nash instantaneous unit hydrographs, in estimating outlet runoff hydrograph characteristics, in Manshad watershed. For this purpose, six storm events were chosen, by using the area’s hydro-meteorological data. Then, the methods’ needing parameters, including the area’s morphometric characteristics and the events properties, were calculated. Thus, outlet runoff hydrographs, for each method were determined. Evaluations that have been carried out by using the mean relative error, the root mean square error and the Nash-Sutcliffe indices, confirmed the higher efficiency of Nash model, in estimating peak discharge, with an error of 23.75, time to peak with an error of 28.21 and the complete shape of hydrograph, with efficiency criterion of 0.4, than the other methods. In addition, this method has a high potential in simulating the actual amount of flood volume, with an error of 9.90. Thus, nash model, compared to other models (based on this study), is the most efficient model for estimating flood discharge.

This study aimed to identify factors affecting the development of gullies and identify the most effective factor and the impact of each factor on the development of gully and gully development predictive models was defined in some regions of Kerman province. At first، 11 gullies in the regions of Baft، Rayen and Rabor are selected and the annual rainfall، width، length، slope bottom of gully، slope of area، the drainage area of gully، soil texture، pH، EC and silicate minerals were measured from the soil layers in the forehead gullies each year. Information obtained for both linear and volumetric expansion mode was analyzed by the SPSS11 software and statistical method regression. It was clear that soil salinity، silt soil، the average width of the gully، gully and slope of the watershed area have the greatest impact on the development of gullies in these areas. Also models showed that the affected factors on gullies development are surface runoff and erosion of soil that have generally occurred because of the effect of land use changes.

Distinguishing the susceptible areas to landslide using different landslide susceptibility mapping (LSM) models is one of the primitive and basic works to reduce probable damages and reduce risk. The main purpose of this research is the efficiency evaluation of four methods including Information value (WINF)، Valuing area accumulation (Wa)، Analytical Hierarchy Process (AHP)، Kopta-Joshi proposed method (LNRF) for LSM in Zangvan watershed، Ilam province. At first، all the effective factors in landslide occurrence were inspected. By analyzing the parameters، nine factors including slope، aspect، elevation، precipitation، distance from road، distance from fault، distance from drainage، land use and lithology were distinguished as the effective factors in landslides occurrence in the studied area. After preparing the information of these nine factors in GIS environment، the location of landslides were determined using areal photographs and satellite images and LSM performed by the above four methods. Finally، the landslide index was used for evaluation the ability of appropriate LSM model. Based on this Index، the information value method classified more 52 percent of occurred landslides in very high danger class. Therefore، this method is more efficient and proposed as the best LSM method in the Zangvan watershed because of compatibility of landslides with high danger classes and ability of differentiation of danger classes.

Evaluation soil erosion is an important matter to protecting it from destroying in future due to the excessive use of the inherent capacity of soil and also improper management. Therefore, in this study land vulnerability related to water and wind erosion of Abyek for crops and pasture land has been evaluated, using Raizal model as one of the Microleis sub-models. Morphological and physical and chemical analysis data were obtained of studying and evaluating the 32 soil profiles. Agricultural - climatic and management information including temperature and precipitation data were collected from Bagh-e-Kosar climatological station for the last 17 years. To study the effect of climatic changes in the assessment of land area for the year 2080 AD (for 70 future year), the reports of International Panel on Climate Change (IPCC) allocated to West Asia have been used. Utilization types considered for evaluation, including wheat, corn, barley and alfalfa. The results obtained of Land vulnerability evaluation studies related to water and wind erosion using Raizal model part have been prepared as maps in the GIS environment. Information obtained from Land vulnerability models related to wind erosion for crops and pasture land, also implies vulnerability risk fora wide percent of the region lands under the current management, that the results evaluation of the proposed management methods express the improvement of destroying ability classes.

Exact recognition of effective factors in hydrological processes such as conversion of rainfall to runoff in the annual scale can improve the accuracy of estimations. Investigations on 13 sub-basins in the Ghezelozan river basin show that the effective parameters of mean annual potential yield (mean annual runoff accompanied by water utilities in basins) are adjusted precipitation، curve number، mean annual temperature، area and basin slope. Meanwhile، this paper focused on adjusted precipitation which is related to the time distribution of water utilities in the basins. Validation processes using runoff data in basins for minimizing the differences between estimated and observed discharge values have been conducted. Those processes recognized two equations according to basin area for validation. Investigations in validation of two basins show that runoff result tolerances are about 5 than that observed discharges

It is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. Hence, at present study, two models including Artificial Neural Networks and nonlinear multivariate regression were used to predict peak discharge in Taleghan watershed. Maximum daily mean discharge and corresponding daily rainfall, one day antecedent and five days antecedent rainfall, sum of five days antecedent rainfall and monthly mean temperature were extracted in Gatehdeh, Mehran, Alizan, Joestan and Gelinak hydrological units and entered into neural network model (from upstream to downstream, respectively). The feed forward network was used with one hidden layer and back-propagation algorithm. Then, the models were trained, validated and tested in three stages. The observed and estimated peak discharges of the models were compared based on RMSE and r. The results showed that neural network has better performance than nonlinear multivariate regression.

The rate of soil erosion and sediment yield depends on different factors. Vegetation cover is more variable factor effecting occurrence of soil erosion in comparison to the other factors in a given watershed. The main purpose of this study was to investigate the role of canopy cover resulting from land management on sediment yield. Taleghan watershed was selected as a study area due to the availability of sufficient data. Necessary information was provided for MPSIAC model and was made in GIS environment and all needed calculations were done for preparing sediment yield map. The result of the comparison of the calculated and the estimated sediment yield indicated similarity of them (i. e. 98. 3% similarity). For impact assessment of vegetation cover effect on sediment control، canopy cover was changed in a range of ±5 to 25% percent with 5% interval in both good and bad conditions. The effect of these changes was applied to bare soil percentage as well. The related thematic maps were prepared and sediment yield was determined. The result showed that sediment yield varied because of changing canopy cover. The increase and decrease of canopy cover by 25%، affected the sediment yield to change by -29. 6% and 26. 8% respectively.

Nowadays، water resources provision with proper quality is one of the important needs. Additionally available water quantity، water resources pollution and water quality are problems about water provision. This research has been done to investigate the factors of ground water salinity and to present a model for estimating groundwater salinity on the Caspian southern coasts. So، these data were provided: water qualitative experimentations in this area، annual precipitation and evaporation، groundwater depth، surface water salinity، aquifer transmissivity، topography and distance from Caspian Sea. Then، Multivariate Regression method was used by using SPSS software. In this stage، groundwater EC has been entered qua a variable for water salinity or dependent variable and ground water salinity factors have been entered qua independent variables. A linear model and a non-linear model were presented. Then، resultant models efficiency were considered by using these models in the areas that (east of Mazandaran province) their data were not used to present resultant models. Finally، groundwater EC average map was provided by using the presented non-linear model and Geographic Information System in the east of Mazandaran province.

The process of precipitation-runoff of each basin, is mostly influenced byhydrologic, geomorphology conditions and zone climate. The measurement, distributionand pattern of precipitation, canopy cover, snow melt and accumulation processcharacteristics, soil layer characteristics and geology formation are the most importantfactors which impact the runoff measurement and basin water balance components. Oneof the general procedures for acknowledgement of precipitation-runoff process is itssimulation by using the hydrologic models and analysis of the results. In this study, itwas tried by using BROOK90, the basin process of precipitation-runoff, snowaccumulation and melt model and also the basin water balance components ofKorkorsar are simulated and calibrated then the effect of hydrologic conditions and alsoclimate changes on basin precipitation-runoff process and water balance components arestudied and analyzed.