جستجوی مقالات مرتبط با کلیدواژه « مدل سازی » در نشریات گروه « محیط زیست »

During last decades, despite developing various reports and techniques, due to over expansion of water pollution, hydrological models have become sophisticated tools for water resources management and planning because of their remarkable ability to analyze information and economic savings resulting from their application. In this study, using the SWAT semi-distributed model, which operates on the basis of physical and non-physical processes governing the water environment, quantitative and qualitative modeling was developed for Ardak River which supplies a significant part of water demand in drinking and agricultural sectors of Mashhad mega city. So, this research is going to provide a right tool for future planning and design making.

After entering the information and constructing the model in Swat, for calibration and verification of stream flow, NO3, MINP, TSS, DO and CBOD in SWAT-CUP model, sampling and testing of different water quality parameters were performed in 12 monthly periods from march 2019 to February 2020.

The results showed that except for nitrate, the amount of other water quality parameters such as phosphorus, sediment and carbonaceous organic matter is higher in wet seasons.

The developed model has a considerable ability to simulate the actual condition of the river and it can be applied to define and implement a variety of scenarios affecting water quality for identifying the best practices by testing different options.

Accelerated changes in land use/land cover (LULC) cause changes in environmental dynamics and land degradation. The monitoring and modeling of changes, based on a time-series LULC approach, is fundamental for planning and managing regional environments. The current study analyzed the LULC changes, as well as estimate future scenarios for 2027 and 2037. To achieve accuracy, in predicting LULC changes, the land change modeler (LCM) was used for the Latian Dam Watershed. The LULC time-series technique was specified utilizing four atmospherically-endorsed surface reflectance Landsat images for the years t1 (1987), t2 (1998), t3 (2007) and t4 (2017), to authenticate the LULC predictions to obtain estimates for t5 (2027) and t6 (2037). The LULC classes identified in the watershed were (a) built-areas, (b) vegetated areas, (c) bare lands and (d) water bodies. The dynamic modeling of the LULC was based on a multilayer perceptron (MLP) and artificial neural network (ANN) in LCM. The overal accuracy rate equivalent 80.66, 83.21, 84.32 and 85.12for the years t1 (1987), t2 (1998), t3 (2007) and t4 (2017), and Kappa Index equating to 0.80, 0.82, 0.84 and 0.86 respectively. The results of LULC change analysis showed an increase in the build-up areas; and a decrease in bare lands and vegetated areas within the duration of the study period. The results of this research could help regional planners and managers in the formulation of public policies designed to conserve environmental resources in the Latian Dam Watershed and consequently, minimize the risks of the fragmentation of orchards, vegetated areas and degradation of the valuable resources.

Base flow is one of the most important criteria which is used for assessment, utilization and management of river flow in drought periods. The base flow amount and its variations depends on several factors. Base flow modeling and estimation can be used in assessment of river health as well as planning of surface water utilization. Base flow refers to the flow of the river in times without rainfall. The base flow of the river is the infiltration of groundwater to the river banks or the river bed. The base flow may be significant and flow continuously throughout the year in the permanent rivers. The base flow has reached the underground water table with its deep penetration, and with the increase in the level of the aquifers, it can be connected with the drainage network, they create their own excess water during a long period of time, which sometimes takes several months. Determining the amount of river flow in dry periods, and the proportion of total runoff is one of the important topics in river hydrology. The base flow plays an important role in the river ecosystem, and is critical to human communities and ecosystems. This is especially important for watersheds that are not fed by snowmelt. Different ecological processes occur in different parts of the river's hydrograph. During base flow and during low water seasons, river ecosystems and habitats are dependent on river flow. Land change affect hydrologica; processes such as infiltration rates, groundwater recharge, groundwater and runoff levels. Also, climatic factors can affect the water yield of river basins. The most common method for regionalization in hydrology is bivariate or multivariate regression. Regression analysis is a useful approach to develop the desired factors in the regionalization of ungauged basins. Regression analysis is also one of the most common statistical methods in predicting values based on most important influencing factors. In this study, the multiple regression was used to model the base flow amount and determining the effective factors on base flow discharge. Ardabil province is considered one of the cold mountainous areas and the amount of precipitation in Ardabil province fluctuates on average between 250 and 600 mm/year in different parts of the province. Therefore, the aim of the current research is to model the effective factors in the amount of base flow and its estimation in the watersheds of Ardabil province.

The topographic maps were used to extract parameters of slope, area, average height of sub-basin, drainage density, length of main river, area percentage of different land uses in each sub-basin, precipitation, daily discharge (as independent variables) was used to analyze the factors affecting the amount of base flow. The daily discharge data recorded in 22 hydrometric stations were used. The base flow was calculated from the daily flow hydrograph by one-parameter algorithm method and programming in Excel software. The one-parameter algorithm method is one of the reverse numerical filter methods that are used in the flow rate signal processing, and separating the base flow from the fast flows using a recursive digital filter. In the following, the physiographic characteristics of the basins have been considered as independent variables, and base flow amount has been modelled using regression analaysis. The necessary statistical tests were performed in the screening stage, and the logarithm of the variables and the Box-Cox method were used to normalize the data. Then, collinearity between independent variables was tested using Pearson's correlation coefficient at 99% confidence level and the VIF values has been examined in SPSS software. Therefoe, independent variables with significant correlation (Sig<0.01) and (VIF>10) were excluded from the regression analysis due to collinearity. The multivariable regression model is an extended type of the bivariate linear regression model, in which it is tried to estimate the dependent variable based on several independent variables, Then, the stepwise regression approach has been considered for the modelling purpose. Then, considering base flow as dependent variables, and other physiographic parameters as independent variables, the most suitable methods has been chosen according to the efficiency assessment criteria.

The ANOVA table of the modelling showed that there is a significant relationship between independent and dependent variables. The obtained value showed that the rangeland percentage amount had a significant effect on the base flow amount. The degree of linear relationship between independent variables is measured by the tolerance index. Standardized β shows a very important role in predicting the dependent variable, so the daily discharge variable had a much greater contribution compared to other variables in the estimation of the dependent variable (base flow). According to the results, all the mentioned factors were considered in proposed regression model considering the VIF value less than 10. Based on the value (β), the contribution of independent was interpretated. The results revealed that the discharge, area, the percentage of medium to good rangelands, and the percentage of agriculture-residential area-garden are among the effective factors. The main river length, average height, and average slope were removed from the modelling procedure due to collinearity effect, and then the stepwise multiple regression was performed and the produced model accuracy were proved as significant (p-value <0.005). Also, the results indicate the positive and direct impact of vegetation and land use on the amount of base flow amount. The results of base flow modeling nased on presented model can be used in the ungauged areas adjacent to the studied watersheds in Ardabil province.

It should be noted that the hydrological response of the basins will be different on hourly, daily, monthly or yearly scales. Therefore, modeling can be done in different time scales, which will help to better understanding of base flow contribution in river flow regime. It should also be noted that considering the influence of factors such as extraction or river flow diversion can affect the accuracy of the results. In addition, the effect of human modifications on the change of the contribution of the river's base flow is important that is usually not considered in modeling and will be the source of a significant error. Also, the changes of climatic factors can affect the water flow of the river, and therefore, the study of the changes in the river flow over time can determine the effects of changes in climatic factors.

The present study was conducted to assess the water quality of the Balighli-Chai and Ghare-Sou Rivers, the main rivers of Ardabil Province, Iran. The levels of dissolved oxygen (DO), biochemical oxygen demand (BOD), nitrate (NO3) and phosphate (PO4) were measured and simulated using the QUAL2Kw model during low- and high-water periods. Also, self-purification capacity and total maximum daily load (TMDL) levels of water quality parameters were calculated. The obtained results indicated considerable differences between the levels simulated by the model from the measured data for both rivers. Most of the river sections had low self-purification capacities. Maximum self-purification capacity (%) were: high water period: DO = -226.61; BOD5 = 90/30; NO3 = 99.88; PO4 = 96.49; low water period: DO = -281.71; BOD5 = 89.13; NO3 = 94.74; PO4 = 90.21. TMDL scores for DO, BOD5 and NO3 were higher during the high-water period, but entire sections of both rivers showed high excess loads of PO4. The results showed that most ranges of both rivers didn't have appropriate water quality conditions. Therefore, it is necessary to make proper decisions to control pollution levels and improve water quality in this basin

Direct measurement of saturated hydraulic conductivity of soil is time consuming and costly and today this parameter can be estimated using soil retrieval parameters. Therefore, this study aimed to use regression modeling to estimate the saturated hydraulic conductivity of soil based on grain size parameters i.e. d10, d50, and d60.  
Material and First, 25 soil samples with sandy texture were randomly collected in the spring of 2017 from the agricultural lands of Saqez city and the samples were collected in a container and taken to the laboratory for analysis and hydraulic guidance using the Darcy’s law was calculated. Using the available data, univariate and multivariate regression relationships were fitted on the data and based on the model evaluation statistics, the relationship that had the best estimate of saturated hydraulic conductivity of soil was determined.
The results of this study showed that the linear equation with 3 inputs saturated hydraulic conductivity of soil more accurately than the equations with 1 or 2 inputs. The results showed that the parameter d10 had a more effective role for estimating saturated hydraulic conductivity of soil than the parameters d50 and d60 and the effective parameter for comparison of saturated hydraulic conductivity is called d10.
Discussion and The main purpose of this study was to provide models that can estimate the saturated hydraulic conductivity of soil with cost reduction and time savings with acceptable precision, and in summary, regression modeling can be used to estimate the saturated hydraulic conductivity of soil.

Todays, tourism is one of the significant components of economic and social development in many regions in the world. Despite of high diversity of natural and cultural resources, Iran doesn’t find its real rule in indoor and inbound tourism. Sepidan County in Fars province is not excluded from this fact. Therefore, the present study conducted to investigate the human factors affecting tourism in this city.

In this research, affective human internal factors (strengths and weaknesses) and external factors (opportunities and threats) on tourism in this region were provided on the basis of framework of SWOT method. These affective factors were questioned from experts in Likert scale. These answers were analyzed by structural equation modeling- partial least square (SEM- PLS) to survey rule of opportunities and threats on strengths and weaknesses of tourism.

In according to results, 50 percent of changes in strengths were determined by other factors and 30 percent of tourism weaknesses by opportunities and threats. In the model, rule of external opportunities factor on internal strength factor was positive and significance (p < 0.01), also the effect of external threats factor on internal weaknesses factor was positive and significance (p < 0.05) and there weren’t significant path between other factors in this model.

Tourism in studied area according to the significant effects of external factors on internal factors is in aggressive and defensive positions. Using the new statistical methods like Partial least squares (PLS)‎ helps to determine strategies for the future and guide direction of management actions with more evidence.

The distribution of canopy classes is the most important structural characteristics of Zagros forest stands. Determining the fitting pattern of canopy classes in Zagros forests shows that, the overall status of these forests from the destruction process and ecological sequence points of view at different times. The aim of this research was to evaluate of probability-statistical functions in order to fit canopy classes of trees in Chartagh forests of Ardal city, Chaharmahal ve Bakhtiari, Iran.

For this purpose, an area of 50 hectares in Ardal forests was selected (3157 trees) were fully callipered and statistically analyzed. Exponential, Gamma, Normal, Beta, Weibull and Log-normal probability distribution patterns were fitted to crown canopy classes. Characteristics of distribution patterns were estimated using maximum likelihood method. Kolmogorov-Smirnov, Anderson-Darling and Chi-square tests were used for comparing of actual probability and probability which derived from functions.

The results of fitting tests showed that log-normal probability distribution was suitable for canopy classes modelling in Chartagh forests.  

Log-normal probability distributions can be used for those who want to simulate changes of forests

Many cities in our country, including Tehran, are facing the phenomenon of obsolescence and inefficiency of urban neighborhoods. Such areas can have devastating environmental, economic, or social effects. In the current era, proper management of such urban spaces requires up-to-date and valid data. Due to the large size of outdated and inefficient neighborhoods, collecting and updating this amount of data by organizations is time-consuming and costly. Therefore, using the capacity of citizen participation is a good opportunity for city managers, which is the main purpose of this research.

Citizen-centered GIS strategy has been introduced in recent years, and in many applications, enterprise location data acquisition has been replaced by volunteered geographic information (VGI). This research presents an object-oriented design for collecting spatial information about urban deterioration. Accordingly, a web-based implementation based on open-source components is on the agenda.

With the help of the proposed solution, first, spatial information can be collected in a categorized and thematic manner. According to the definition of worn-out texture, ten different classes were considered in the object-oriented approach, which include undesirable paths, unsafe and old buildings, ruined and dirty lands, narrow access networks, and places for offenders and addicts. A web-based system was also implemented based on open-source frameworks such as Django and components such as GeoDjango, PostGIS, and OpenLayers.

Citizen science is a new approach to collecting spatial information on worn-out tissue. The result of the implementation of the method used is the rapid and low-cost provider of information and enhances the role of experts in municipalities and urban design organizations, mainly professional supervisors instead of data providers. Cost-benefit analysis showed that in a one-year period, this approach could lead to an approximate savings of 15%.

Nowadays, due to the expansion of societies and consequently increased use of water resources, abnormalities in rivers have increased and eventually led to changes in water quality conditions. Therefore, it is essential try to develop a management system to predict the pollutants behavior and also prevent excessive environmental pollution. The aim of this research is to model the changes of water quality parameters of Karaj River using CE-QUAL-W2 model. In order to achieve the research goal, hydrodynamics of the system (water velocity) and river quality parameters including nitrate, phosphate, algae, ammonium, dissolved oxygen, biological oxygen demand were calibrated and then verified. The results show a suitable matching with observed data. Moreover comparing the results of simulated concentration of DO and BOD with the standard level of these parameters for surface water is an evident for the health of the river. Considering the accurate predictions of the model, it can be concluded that utilizing of appropriate mathematical models with spending less time and cost compared to field monitoring and laboratory observations for qualitative simulation and representing management scenarios would be a very useful tool in water resource management and would help the planners and managers as a decision support tool to achieve the goals of sustainable development.

Industrialization, urbanization, and population growth are considered as the main causes of urban air pollution that is responsible for millions of deaths per year worldwide. Besides, the impact of urban air pollution on health is considerable. Respiratory, lung diseases, and heart attacks are largely due to urban air pollution. However, there is a lack of air pollution monitoring stations (hereafter simply stations) in most cities worldwide because of their high expenses, and, thus, access to high spatial and temporal coverage of air pollutants and their distribution is limited. To address this issue, the main purpose of this study was to estimate CO concentration in Isfahan, Iran, based on air pollution monitoring stations and Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2018 to 2019.

In the present work, we used ANFIS and Random Forest (RF) algorithms to estimate the CO concentration level. To implement the ANFIS algorithm, based on collected air pollution data from the stations and Aerosol Optical Depth (AOD) data from MODIS imagery, the basic fuzzy rules were extracted. Further, with the integration of fuzzy rules and artificial neural network algorithm, ANFIS algorithm was implemented to model the dispersion of CO level in Isfahan city. To model the dispersion of CO using the RF algorithm, air pollution data and AOD data were used. Since the number of trees and the number of variables in each node are two basic parameters in the success of the RF algorithm, a 10-fold cross-validation method was used to identify value for these two variables.

Our findings indicated that the RF algorithm was more efficient and accurate in spatial modeling the dispersion of CO because it achieved better RMSE and MAE results than the ANFIS algorithm. The RMSE error value of the RF and ANFIS algorithms were 0.724 and 0.809 ppm, respectively. Furthermore, the MAE error value of the RF and ANFIS algorithms were 0.636 and 0.792 ppm, respectively. In the case of spatial dispersion of CO pollutants, the ANFIS algorithm showed that the amount of this pollutant varies in the city. For example, the central and northern regions of Isfahan had the most pollution and the eastern and western regions of Isfahan had the least pollution based on the ANFIS algorithm. Regarding the RF algorithm, it was observed that by moving from the southeast to the northwest of Isfahan, the amount of CO pollutant increases, and the northwestern regions of Isfahan had the highest CO pollution. The examination of numerical values obtained from the ANFIS algorithm showed that the lowest amount of CO pollution in Isfahan city was equal to 1.43 ppm and the highest amount was 2.13 ppm. In contrast, obtained results from the RF algorithm showed that the lowest amount of CO pollution in the city was equal to 0.57 ppm and the highest amount was 2.27 ppm.

Overall, it can be concluded that since ANFIS and RF algorithms are appropriate and accurate methods in modeling environmental problems due to their nonlinear modeling, the ability to reduce the negative effects of outgoing data, and less sensitivity to the local minimum problem. It should be noted that a significant part of the error observed in the results of ANFIS and RF methods was related to the intrinsic properties of MODIS imagery (i.e., cloud cover and mixed pixel problem due to the coarse resolution of MODIS imagery), point measurements of air pollution data collected from the stations, and recorded data error at the stations.

Evaporation is one of the wasteful methods of water resources in geographical areas and is of special importance in the study of water resources.

In the present study, databases including Chah Nimeh dam evaporation data and large-scale network data have been prepared. The SDSM model is used to simulate the evaporation of the coming decades under three scenarios: RCP2.6, RCP4.5 and RCP8.5. The basic modeling period is from 1983 to 2005 (23 years)

Comparison of evaporation estimates for the next two time periods and under different scenarios showed that for the time period 2100-2080 scenarios RCP2.6 and RCP8.5 estimated higher values for evaporation. Examination of inputs showed that air temperature, geopotential height and wind indices have the greatest impact on the evaporation of wells in Sistan.

The results of this study showed that the rate of evaporation in the period of increasing 2100-2080 will experience more than 300 mm per year. The greatest increase in evaporation will be in the warm period of the year.

The detection of changes in climate parameters indicates that climate change has begun in Iran and it is necessary to identify the habitats of potential species, present and future years, under climate warning models. In this regard, it should be examined whether the increase in temperature in the country will have a positive effect on the presence of species in rangeland habitats, or a negative effect?. For this purpose, the current and potential future range of Artemisia Aucheri for the next three decades (2050) were predicted under two climate warning models (Rcp4.5 and Rcp8.5).

In this connection; First, the event map (presence and absence) of A. Aucheri species was prepared at the level of rangeland habitats. Then, the values of predictive environmental variables were calculated at the species occurrence site. For this purpose, 19 bioclimatic variables and three physiographic variables (slope, direction, height) with a pixel size of 4.9× 4.9 km were used to model the distribution. The points of presence were marked with the number one and the absence with the number zero. Then, considering the values of each of the climatic and physiographic variables as independent variables and the amount of presence and absence of the species as a dependent variable; a regression relationship between species occurrence and environmental variables was calculated. In the next step; based on the resulting regression relationship, prediction maps of the current (present) range and the potential future range of A. Aucheri species for the next three decades (2050) were prepared. In this regard, data related to monthly temperature and precipitation for the base time period (current) and the future time period, with a resolution of 30 seconds, in small scale from the WorldClim.org database, under two climate warning models (milder scenario and The more severe scenario) (Rcp4.5 and Rcp8.5) was downloaded from the General Atmospheric Circulation Model (CCSM4) and the maps were modified using temperature and rainfall data from synoptic meteorological stations. Then, by modifying the temperature and rainfall layers, layers of 19 bioclimatic variables were generated in DIVA-GIS software. From the digital elevation model map in the GIS environment, slope percentage and slope direction maps were generated and used as physiographic variables. All peripheral layers were standardized in Arc-GIS software in terms of area, number of pixels and image system. Next, the calculated environmental variables were placed in the regression relationship presented in the previous step, which was obtained between species occurrence with climatic and physiographic variables. Then, in the context of the Biomod package in the R program; the statistical model was transformed into a species distribution map, and in accordance with the fitted values, in Arc GIS software, a map was generated with a value between zero and one, and values close to one indicate areas with high probability of occurrence of the species. To better map the current range and future potential range; output maps, with a probability of occurrence between zero and one, to four floors; Inadequate habitat (0-0.25), almost suitable habitat (0.25-0.5), highly suitable habitat (0.5-0.75) and very high fitness habitat (0.75-1), The grouping was intense and by referring to the coefficients of variables in regression relations, effective variables for the current range and potential future range were introduced. In this regard, the extent of conformity of the presence range of A. Aucheri species with the current range (present) and their potential range in the future was evaluated using kappa statistical coefficient.

Global warming due to increased greenhouse gas emissions; it has caused obvious changes in Iran's climate, including rising temperatures, increasing climatic hazards and decreasing rainfall, in the last two decades. In this regard, most scientific sources emphasize that the increase in temperature has reduced the coefficient of bioclimatic comfort, and some plant and animal species that are not able to adapt to climate change, they migrate from their permanent habitat or gradually disappear. In this study, this aspect of the issue, namely climate change and the distribution of plant species, was emphasized. Based on the results; among the climatic and topographic variables, only the temperature-related indicators, in order of importance, include; BIO9, BIO10, BIO5 and BIO1 were identified as effective on the distribution of A. Aucheri species. The values of the characteristics increase as the climatic conditions worsen and as a result, the suitable habitat level (more than 75% probability) of A. Aucheri species will decrease for the following decades. In other words, it is shrinking in response to climate change. In this regard, the suitable habitat area of this species (probability of occurrence more than 75%) according to the current forecast map is 274505 hectares, which is about 18% of the total rangeland habitats in the southern Alborz. Also, the suitable habitat area of this species according to the forecast maps for 2050, under two scenarios of Rcp4.5 and Rcp8.5, is 208175 and 131964 hectares, respectively, which has about 13 and 8% of the total rangeland habitats. Overall; the area belonging to the classes in which A. Aucheri is more likely to be present has decreased in 2050 than at present. This indicates that in the future, the presence of A. Aucheri species in the habitats of the region will decrease and the risk of its removal from the ecosystems is evident. In sum, climate change and the consequent increase in temperature characteristics will cause the vertical expansion of A. Aucheri species and its movement towards higher latitudes along the altitude gradient of the region. Hence, the lower (1100 m) and upper (2500 m) expected range of vegetation range and altitude range in which A. Aucheri was observed (3400-1800 m); it will change over the next three decades. In this regard, the expansion of plants to higher altitudes during recent periods, an example of species displacement under the influence of climate change, has been mentioned. Also, one of the most important effects of climate change is the displacement of the geographical area of plant species. Based on the results of the present study and similar studies, by determining the degree of overlap of the current range event with the potential future range (for the next decade) of plant species, according to two climate warning models (milder scenario and more severe scenario); The changes that will occur as a result of climate change over the next decade in the distribution of species can be examined. Also, the results will be applicable in conservation and vegetation restoration programs, and based on the results, it is possible to identify suitable climatically and topographically sites for species conservation and rangeland.

In recent decades, the potential danger of heavy metals in effluents and the entry of water into surface and groundwater resources have been increasingly being considered by the international community. The aim of this study is to provide an indirect method for estimating the efficiency of absorption of rubber chips filter for heavy metals lead, zinc and manganese from industrial effluents. Column adsorption test in a pilot system was conducted as a factorial experiment with three factors based on a completely randomized design with three replications. Three factors were studied including particle size (0.5 and 5 cm), filter thickness (10, 30 and 50 cm) and sorbent contact time with solution. The adsorption of the elements was modeled using 6 models of linear regression, regression tree, artificial neural network, random forest, cubist and support vector machine, based on the Field absorption experiments data. The results showed random forest models, artificial neural network, regression tree and cubist had acceptable performance for predicting adsorption efficiency in all three elements. However, according to the R2 coefficient and RMSE, random forest and artificial neural network showed more satisfactory performance than regression tree and cubist model. Evaluation of the importance showed the high importance of the parameter of adsorbent contact time with the metal solution in all machine learning models. The accurate predicted ability of developed models could significantly reduce field experiment workload such as predicting the removal efficiency of rubber chips. The relative importance of variables could provide a right direction for better treatments of heavy metals.

In this study, fuzzy linear and fuzzy least-squres regression approach was employed to estimate the monthly reference evapotranspiration of Neyshabour plain. The data used, including maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), relative humidity (RH), solar radiation (Rs) and wind speed (U2), were obtained from synaptic meteorological station of Neyshabour. Three different scenarios were designed to estimate the evapotranspiration for either fuzzy linear or fuzzy least-squres regression models. Mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance fuzzy regression models and its comparison with FAO-56 Penman-Monteith. Results indicated that the fuzzy linear regression model in January and the fuzzy least squares regression model in October had the highest and lowest accuracy with R2 of 0.903 and 0.502, respectively. Among the new proposed models, the fuzzy linear regression under scenario FLR1 (Inputs included Tmax, Tmin, RH and U2) had the highest accuracy, however, in both regression models, despite having lower input parameters (Tmean, RH and Rs), the second scenario, was comparable with other and therefore it can be used in data deficit conditions as an optimal approach in determining ETo for irrigation planning and water resource management.

Most of the vertebrate species populations affected by the main threatening factors like habitat destruction and fragmentation are experiencing rapid and worse destruction and decrease. Identification of potential and suitable habitats trough modeling tools and methods as well as survey on population relationship and contacts through genetic studies, provides needed knowledge and understanding to define the conservation status and needs for different species. For these purpose, Maximum Entropy (Maxent) and mitochondrial cytb sequencing with 704 bp were deployed. Based on the results, northern part of Iran followed by Semnan, Tehran, Qom and Alborz provinces, as well as western part including Khuzestan consisted the most suitable habitats for the species. Haplotype diversity of h= 0.894 as the main index, showed high genetic diversity for the populations of the species in Iran. Populations of Stripped Hyena, in Iran, are losing their main and suitable habitats due to habitats destruction and degradation, illegal hunting and lack of proper genetic connections. Decreasing populations and worse conservation status of the species convince us to prepare and conduct urgent conservation actions to prevent more loss. The study, using the habitat modeling and genetic surveys, as well as data gained from the field works, gives new vision on the conservation actions needed to protect stripped hyena in Iran

Desertification involves processes that are both the product of natural factors and the mismanagement of human beings. Adafic parameters and processes affecting soil condition, climate, groundwater, vegetation and management are the most important factors affecting the phenomenon of desertification in many arid and semi-arid regions. These parameters are investigated using different models in different regions.Therefore, this study was conducted to evaluate the desertification intensity based on groundwater and soil criteria in the west of Golestan province.

To determine the work unit map, topographic, geological, aerial photo interpretation, panchromatic band and multispectral Landsat satellite images and field visits were used. In this study, the Madalus method was used to prepare a map and evaluate the desertification situation. The geostatistical methods used in this research include kriging method, local estimator method (GPI), radial function method (RBF) and distance distance method (IDW). in Madalus model, 6 criteria and 20 indicators were used to evaluate the intensity of desertification. The scoring of each of the indicators was determined at the unit level in the region. The map of each criterion is also obtained through the geometric mean relationship between the indicators. To evaluate the accuracy of the model results, the desertification class in each of the work units according to the field evidence, observational and visual evaluation was performed and then the statistical comparison of the model desertification class with an expert opinion was performed. SPSS software and Mann-Whitney non-parametric test were used to validate the model results.

In this study, according to the histogram of the data, the normality of the parameters was investigated. The mean square squared error (RMSE) was used to determine the most appropriate interpolation method. The weighted average score of desertification intensity of area 135 was obtained, which indicates the middle class. In terms of zoning of desertification intensity, the region was classified into three classes: low and insignificant with a frequency of 27 and medium with a frequency of 60 and severe with a frequency of 13. Among the criteria of desertification, the criterion of management and policy with an average weight of 148 points is the dominant and effective criterion of desertification, followed by the criterion of vegetation (145), the criterion of soil (141), the criterion of erosion (138), the criterion of climate. (122) and groundwater criteria (121) were in the next rank of effective desertification criteria in the region. Also, the most important indicators of desertification are the indicators of drought resistance, conservation operations and soil salinity, respectively. These indicators have increased the trend of desertification in the work units of abandoned lands, saline and wetland lands and saline lands located in the northeastern parts of the region. Based on the spatial distribution of classes with low and insignificant desertification intensity in the southern and eastern part of the region, the middle class in the western, central and northern parts and finally the severe desertification class are located in the northeastern parts of the region.

According to the obtained results, which indicate high evapotranspiration of the region, expansion of land salinity, unprincipled road construction and incomplete drainage.it seems that the management of desertification in the west of Golestan province, as one of the agricultural hubs, should be in the managerial priority of the officials and experts of the executive departments.Accordingly, it is proposed in order to control the process of desertification and achieve sustainable development in the region.treatment of industrial and domestic effluents for reuse for various purposes, Use of modern irrigation systems for agricultural lands, Placing low-yield crops in terms of water consumption in the region's crop rotation and As well as the necessary training to justify farmers to use pesticides and chemical fertilizers in the area.

Climate change is recognized as one of the largest threats to biodiversity and is expected to result in shifting species distributions and increasing extinction risk of populations that are unable to adapt or relocate to suitable habitats. In the present study, habitat suitability of the yellow-spotted mountain newt was modeled to identify the current suitable habitats, to predict the expansion or contraction of the distribution of the species under future climate change scenarios, and to determine the main environmental variables.

The studied species inhabit 57 highland streams of the Zagros Mountain range in western Iran and eastern Iraq. In the present study, species distributions were examined using the maximum entropy model (MaxEnt) for the current and the future (2050 and 2070) climate projection under two optimistic (RCP2.6) and pessimistic (RCP8.5) scenarios.

Based on the results, precipitation of the coldest quarter (BIO13) was the most percent of contribution to predicting species distribution. Comparison of suitable habitat areas in the current and future climate conditions showed that suitable habitats area for the species will not significantly decrease under the optimistic (RCP2.6) scenario in 2070 and an increase was also observed in 2050. Although, a reduction in suitable habitats area (more than 50%) was observed in 2070 under pessimistic (RCP8.5) scenarios. The results of the model supported the hypothesis that due to future climate change, the species distribution range is likely to be conserved in the refugia located in the center and south of the distribution range and by shifting to higher altitudes.

Results obtained from the current study supported the hypothesis that the Zagros climatic refugia play an important role in conserving populations as well as habitats suitable for the Yellow-spotted mountain newtduring climate change.

 In designing ecological industrial parks, an attempt is to maximize the self-purification of the system by defined mechanisms in order to reduce the pollution produced by industries first and then, to reuse the by-products of industrial processes. Due to the high vulnerability of the coastal sector, species diversity and rapid sequence in the region, the use of environmental methods in the environment of shahid salimi powerplant (Neka) is inevitable. The purpose of this study is to calculate and model the distribution of SO2, NOx pollutants to be displayed seasonally and annually using ADMS software in the form of GIS layers and to achieve the design criteria of an eco- industrial park in the study area, the mass flow of SO2 and NOx is calculated in gr/sec and also the flue smoke of the powerplant fuel consumption is analyzed in cold and hot seasons of the year separately. The results showed that in Neka power plant, the average concentration of NOx gas per unit of steam is 304 μg/m3 and in combined cycle unit is 77.75 μg/m3 and also the average concentration of SO2 per unit of steam is 267.25 μg/m3 during the year which does not meet any of WHO and European clean air standards.

Air pollution is a global threat to public and environmental health, especially in urban areas. Therefore, modeling is used to control and planing concentration of pollutants. In this paper, a model is proposed based on linear regression for short term forecasting of CO, PM10 and O3 based on meteorological parameters, and the results are presented. Data of meteorological parameters including humidity, pressure, minimum and maximum temperature, wind speed and wind direction (Birjand Meteorological Organization), and air pollution data (CO, PM10, and O3 concentrations) from the Birjand weather organization were prepared and used as daily average. SPSS16 software was used for linear regression modeling. The results showed that the highest correlation coefficient for CO pollutant with minimum temperature was 0.53 and the lowest correlation coefficient with the value of 0.166 was wtih the wind direction. The maximum correlation coefficient of PM10 contamination with wind speed was 0.33 and the lowest correlation coefficient of this pollutant with a pressure was 0.882. Finally, the highest correlation coefficient of O3 contamination with maximum temperature was 0.50 and also with regard to the regression coefficient obtained for carbon monoxide (R = 0.33) compared to the other two pollutants, has been obtained better.

The urbanization Phenomenon is expanding rapidly in most parts of the world due to population growth. The migration from rural to urban areas and, consequently, urban development has become a significant challenge worldwide. Urban development and changes in land-use patterns cause social and environmental impacts, Such as reducing natural and agricultural land with high production capacity, water quality, and local climate change. All of these are somehow related to changes in land-use patterns due to human activities. Because of extensive effects on the environment, water cycles, and natural habitats, understanding the quantity and spatial patterns of land-use/land-cover changes is vital. On the other hand, studying these effects in coastal areas with ecotone and more sensitive habitats is more important than in other areas. In this regard, models are a suitable tool to express the complexity of development processes in simple terms.
Cellular Automata Models (CA) are dynamic tools in modeling natural and physical features of the land surface and have been widely used in predicting land-use change and urban development. In all of the CA models, the SLEUTH model is probably the most appropriate. It is a hybrid of two CA models that can model urban development and land-use change simultaneously. The SLEUTH model was developed to simulate urban growth based on automated networks. The abbreviation of the SLEUTH included its six input layers: slope, land- cover, the excluded area from development, urban areas, transportation network, and hillshade layer. There are two general steps in implementing SLEUTH: the model calibration to extracting patterns and trends in the past and the forecasting step in which past trends are used for the future. The original version of the SLEUTH model has some constraints such as the number of historical data required, memory limitation, the problem of the road search algorithm with spending time and more memory. This model also tends to the edge or organic growth, so it cannot produce a good level of dispersed growth. These constraints in the latest version of the model (SLEUTH-3r) have been fixed or improved.
Reviewing the studies conducted in Iran shows that the new version of this model has been used in none of these studies. Also, modeling and forecasting urban growth in the coastal areas of Iran has not been done so far. Overseas studies have not been conducted in coastal areas too. Therefore, this study aims to identify historical patterns and predict future urban growth and land-use change in a part of the Persian Gulf coastline.

Case Study
Parsian city is located in the southwest of Hormozgan province. The population of the city is 50,596 people. Parsian, with about 1619.5 square kilometers of area, two districts, two cities, four villages, and 42 towns, constitutes about 2.7% of the total population and 2.2% total area of Hormozgan province. Parsian coastal area includes large parts of Kushkenar, Behdasht, Buchir, and Mehregan. With an area of 1021 square kilometers, this sub-area involves about 63% of the Parsian city.

The steps for setting up the SLEUTH-3r model are as follows:• Preparation of model database and its validation
• Model testing
• Model calibration
• Prediction validation
• Model Prediction
• Preparation of SLEUTH-3r model database:In order to model the development of cities in the study area by the SLEUTH-3r model, the following maps should be prepared: Historical urban maps and their validation
 Historical land-use maps
 Historical maps of the transportation network
 Slope map
 Map of excluded areas from development
 Hillshade Map
• Model testing:The test scenario file was defined, and then this test was performed to check the model installation and ensure the layers setting in the model.
• Model Calibration:In the SLEUTH-3r model, the coefficients of diffusion, breed, spread, slope, and roads gravity affect how the growth rules are applied. These coefficients are calculated in the calibration step and on historical maps. The initial coarse calibration step was performed with 10 Monte Carlo. The SLEUTH-3r version uses two measures: Cluster Fraction Difference (CFD) and Area Fraction Difference (AFD). Then, by obtaining suitable coefficients, another execution was performed by setting 100 Monte Carlo to average these coefficients. The coefficients obtained in this step were used in the forecasting process.
• Prediction validation:In this step, the model was implemented with a historical city map in 2013 to obtain a future city map in 2019.
• Model Predicting:Ensuring the accuracy of the model prediction, the model with the historical city map in 2019 as the start year of the forecasting was implemented to obtain the city map in 2050.

Validation of historical urban maps shows that all of them have high accuracy. The accuracy of these maps in 1984, 1993, 2002, and 2019 is 99% and in 2013 is 98%. The calibration section lasted about 60 hours. Also, the value of AFD and CFD were about 0.1 means that the fraction difference between the modeled clusters and the modeled urban areas than observes one is the lowest. The distribution coefficient of 25 indicates the low probability of city distribution outside the main urban centers. According to the breed coefficient (50), about half of the city's growth will from the vacant land within the city; this means the possibility of the city's growth through its edges is low. Also, the relationship between urban growth and transportation networks is linear, and the effect of slopes on the city's development in this region is high. Also, the kappa statistic (0.99) shows that predicting urban growth has been done with great accuracy. Urban growth in the Parsian coastal area will occur with a very high rate (73.58) from 2019 to 2050. The urban area will increase to 2281.2 hectares and reach about 3481.4 hectares during these 31 years, indicating the rapid and extensive development of residential places in this area. The trend of changes in residential places over 66 years shows that the rate of urban growth from 1984 to 2019 had a slight slope, while from 2019 to 2050, increasing sharply, Which indicates the significant population changes followed by the rapid development of human settlements. Over 31 years, 47.14% of the barren lands will become urban; this confirms a breed coefficient and a road gravity coefficient of 50. Also, 4.96% of the vegetation area will be converted into urban, which indicates the conversion of urban vegetation into residential areas.

This study was performed using the SLEUTH-3r model, which is an improved version of the SLEUTH model. The pattern of urban growth in this region is most affected by the slopes, and the growth of residential places has a linear relationship with the transportation network. Urban areas will increase from 1,200 hectares to 3,481 hectares from 2019 to 2050, equivalent to a growth rate of 73.58 and 2281 hectares. Furthermore, about 47% of the barren lands area will be turned into urban. This rapid growth trend will lead to other environmental changes, and it will be accompanied by an increase in the needs of future residents. Therefore, policymakers and land planners must be fully aware of these issues and their consequences. These results can provide a practical perspective for planning land and be an essential role in guiding sustainable urban development in this region.