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

It is necessary to evaluate, identify and prioritise areas prone to erosion. However, the nature of soil erosion and its processes have necessitated effective water and soil conservation strategies, which require hazard assessment at various temporal and spatial scales. Therefore, the current research utilizes the PAP/RAC qualitative model to predict and determine the water erosion hazard status of the Qharnaveh watershed. It also investigates the uncertainty of the factors affecting the erosion hazard map obtained from this model using Bayesian Model Averaging (BMA). This is done to enhance the evaluation, identification, and prioritization of points at risk of water erosion, thereby providing effective strategies for water and soil conservation to mitigate the trend and development of soil erosion.

The Priority Actions Program/Regional Activity Centre (PAP/RAC), established in 1977, is one of the six Regional Action Centers of the Mediterranean Action Program (MAP), which is itself part of the United Nations Environment Program (UNEP). The MAP includes 21 Mediterranean countries and the European Union, with a common objective of creating a healthier Mediterranean environment on the basis of sustainable development principles. The main area of PAP/RAC is to support Mediterranean countries in improving the sustainable management of their coasts, in particular through the adoption and implementation of the Integrated Management Protocol in the Mediterranean Coastal Zones. In 1998, the PAP/RAC model and approach were presented and employed as a PAP/RAC. According to the executive structure of the PAP/RAC model, in the first stage (predictive approach), in the first step, a Watershed slope map was prepared from a DEM at a height of 30 m, and based on the valuation manual, it was divided into five classes. In the second step, the lithological map was prepared from 1:250,000 geological maps of Golestan province, which was divided into five classes based on the valuation manual. In the third step, the slope erodibility map, which is the result of integration of the rock unit map and the slope map, was evaluated and prepared according to the instructions. In the fourth step, a land use map was prepared based on the classification of the Sentinel 2 satellite images of August 2023 in the Google Earth Engine along with the existing regional information and divided into four categories: residential, forest, pasture and agriculture and evaluated according to the instructions. In the fifth step, the vegetation density map for August 2023 was calculated using the Normalized Difference Vegetation Index (NDVI) and evaluated according to the instructions. In the sixth step, integrating the land use map and vegetation density map according to the instructions, the soil conservation map was created. In the seventh step, a predicted erosion hazard map was prepared by integrating the erodibility map (integration of slope and lithology) and the soil conservation map, considering the influence of slope factors, lithology, land use, and land cover. After creating the erosion hazard map of the region, field survey and aerial photos were used to assess the erosion states, facilitating the implementation of a descriptive approach to the model. Finally, by overlaying the erosion states on the erosion hazard prediction map, the model integration approach was executed. For the second approach of this research, the area of the watershed (each hectare) was considered as the size of the statistical population. Cochran's formula was utilized to determine the number of random points. Using the 'Create Random Points' command corresponding to the boundary of the Qharnaveh watershed in the ArcGIS software, random points were placed. Based on the value of each point in the model factors (slope, geology, vegetation, and land use) using the BMA method, the impact of each factor on the water erosion hazard map was determined in the R program environment.

The results of the PAP/RAC qualitative model showed that 0.19, 3.34, 34.94, 227.28, and 514.53 km2 of the watershed area were classified as very low, low, moderate, high, and very high erosion hazard, respectively. In other words, 741.81 km2 of the watershed area exhibited high and very high erosion hazard. Generally, according to the factors of the PAP/RAC qualitative model, the results indicate the high potential of this watershed for erosion and soil loss. Further, examination of uncertainty with the BMA reveals the significant importance of slope and lithology in the model output.

Considering the importance of evaluating erosion-prone points, it is recommended that the assessment of PAP/RAC model factors in the field should be conducted more meticulously. Additionally, if feasible, model guidelines tailored to the conditions of Iran should be developed to devise more effective water and soil conservation strategies based on the model results, aiming to mitigate the trend and development of soil erosion.

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.

In the 3 last decades, there were efforts to consider the economic value of ecosystems in the framework of virtual markets, in order to have a better recognition and protection toward them. Therefore, the authors of current study tried to use conditional valuation and mutual 2-dimensional technique, since they have a good knowledge about the valuable services of forest ecosystem of Sheikh mousa in Babol city. 157 questionnaires were distributed among the population (eastern banpey region in Babol) in 2017-2018 period. The factors effective on propensity to pay to stakeholders based on Protective valuation scheme had been investigated. In addition, sampling was performed according to Michel & Carson’s method. The current study is quantitative and results were analyzed in maple 18, shazam 11 & excel 2016 software. The consistency and reliability of information resulted from logit pattern showed that proposed price, age, not being the head of family, visiting the area and environmental ethics are the main effective factors on the propensity to payment for protecting the forest ecosystem of sheikh mousa region. In addition, the annual propensity toward individual payment is equal to 7892.92 Rials (1.87 dollars), and the overall protective value of mentioned ecosystem is more than 2276 million Rials (66096 dollars). In addition, the minimum voluntary investment (available capital) to protect the forest ecosystem of sheikh mousa, from the preference perspective of stakeholders of related society is around 1289 million Rials. According to the obtained results, it can be argued that protecting from this forest ecosystem because of its valuable services from stakeholders perspective is very important. Therefore it is suggested that by establishment of NGO and workshops in the city by corresponding authorities, the environmental ethics and tendencies of people toward protection of such ecosystem get changed. In addition, performing more studies about other applications and services of these type of ecosystems is proposed, in order to have a higher and better understanding in public community and responsible managers about these ecosystems.

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.