جستجوی مقالات مرتبط با کلیدواژه "simulation" در نشریات گروه "جغرافیا"

Hydrological models are an effective tool for managing water resources as well as water balance components among research works. Today, basin hydrological models have been developed, but choosing the right model to simulate a specific basin has always been a challenge. Therefore, it is necessary to choose a model that can simulate the hydrological processes with the simplicity of the structure and using minimal factors. Northwest Iran was simulated by AWBM and SWAT models. The AWBM model is an integrated model that simulates the runoff in catchment areas using two variables, rainfall and evaporation, and on the other hand, the SWAT model is a continuous and semi-distributed model that simulates hydrological processes using the physical characteristics of the basin (soil, land use, slope) as well as several water and meteorological information such as rainfall, temperature. The results of runoff simulation in the calibration and validation periods were evaluated using two Nash Sutcliffe statistical indices (NSE) and R2 coefficient of determination. By comparing the results of the statistical indicators used in the study, it was found that the SWAT model has better results in the simulation of monthly runoff in the validation and validation periods.

The ideal development of the rural tourism industry requires an extensive knowledge of its key determinants and the associated issues. Against this background, this study was to present a model for simulating rural tourism development (RTD) in Deh-e Bala villages, Taft County, Yazd, Iran, given the major determinants. Utilizing a mixed-methods (qualitative-quantitative) approach, this applied study implemented a descriptive-analytical design. The statistical population in the qualitative part comprised university professors and tourism managers, but only university professors were recruited in the quantitative part. Snowball sampling was also practiced to reach theoretical saturation in the qualitative part, while purposive sampling was performed in the quantitative one. The data collection tools included interviews and questionnaires. In the qualitative part, thematic analysis was completed to identify the RTD determinants, which were then prioritized via the fuzzy Delphi method (FDM). At last, the RTD model was simulated in the quantitative part through agent-based modeling (ABM). The data analysis was done using the MAXQDA 20 software in the qualitative part and the Microsoft Excel and AnyLogic software packages in the quantitative one, contributing to the emergence of 44 primary codes, ten sub-themes, and four main themes (i.e., managerial, infrastructural, economic, and social factors). In this context, the managerial factor was labeled the most effective one. Moreover, the simulation output revealed that the RTD rate in Deh-e Bala villages, Taft County, Yazd, Iran, was over 95%. Along with the study results, tourism managers are suggested to dedicate much more attention to the key determinants and their leading roles in RTD.

Digital twin is one of the groundbreaking media innovations in the city domain, which allows us to plan for complicated urban challenges using modeling and a virtual depiction of the actual environment, which reflects the idea of a smart city. Most of the studies and real-world experience demonstrate that such a technology is within the scope of urban management organizations and aligns with notions of smart cities and urban sustainability. With regard to resources, this technology medium seems to have the potential to lend itself to shaping the urban environment through the generation of meaning and urban narratives. In light of this information, the study aims to understand if it is possible to influence citizens’ perceptions of the city through urban digital twin technology?. This qualitative research aims to establish a link between accepted assumptions and established concepts from earlier studies. For this purpose, this study draws from available literature to generate a new assumption using the inductive research approach. The research’s conclusions demonstrate that by replicating a physical counterpart and modeling the actual world, it is possible to display a hyperreality parallel to what is happening in reality, giving the user a unique virtual experience. Interestingly, this experience, which is associated with a physical counterpart, can serve as a mental background. As much as the digital twin will be able to create a new interpretation of the place in the user’s mind, this medium can affect the urban landscape or citizens’ perception of it by creating a hyperreality of the real environment.

The pan evaporation is used as a practical parameter in various fields, such as estimating water loss from lakes and dams, as well as estimating the Plant water requirement, especially in areas where there is no lysimeter information. Modeling this parameter can be useful in the estimation of missing data and long-term planning of water resources and agricultural development. In this research, by using an artificial intelligence model (Gene Expression Programming, GEP) and two time series models (Fourier and ARIMA), evaporation from the pan at Zayanderud dam station, was modeled in the period from 1344 to 1396 (53 years). The time series of pan evaporation on daily scale for the months of June (Khordad), July (Tir), August (Mordad), September (Shahrivar), and October (Mehr) as input to the Fourier and ARIMA models and 4 different patterns including the use of daily evaporation data 1 month, 2 months, 3 months and 4 months ago, were used as input for gene expression programming model. The results showed that the GEP model has acceptable results only in Mehr, and for other months the results are not acceptable in terms of statistical indicators. The daily evaporation estimated error was found to be 0.38 mm in Mehr. This error was acceptable based on the coefficient of determination of 0.84, the Nash-Sutcliffe coefficient of 0.83 and the Willmott's index of  agreement of 0.95. Unlike the gene expression programming model, the Fourier model provided acceptable results in all the studied months. The error values were obtained between 1.02 and 0.7 mm per day in all the studied months, which is equivalent to 5.2 to 8.8 percent. Comparing the results of the above two models with the ARIMA model showed that, the error values of the ARIMA model in all months are higher (9.4 to 19.6%) than the Fourier model, and Gene Expression Programming model. Therefore, the best model for estimating pan evaporation in October (Mehr) is the Gene Expression Programming model and in the rest of the months, the Fourier model. The evaluation of the model in estimating the extremes daily evaporation data also showed that the highest and lowest accuracy is in October (Mehr) and June (Khordad), respectively. In total, according to the statistical indices, the ability of the Fourier model to estimate the daily evaporation in the Zayanderud dam station was proved. Therefore, this model can be recommended to estimate the daily evaporation and also to reconstruct the missing data in this station

The importance of industry and its role in a country’s economic growth has been considered as one of the most important economic issues by researchers. In the meantime, the development of industry in Yazd Ostan and the over-establishment of industries in the Yazd-Ardakan plain have caused damage to water resources and increased pollution in this region. This study aims to investigate the resilience of water resources in the Yazd Ardakan plain in terms of industry establishment. In this research, industrial development has been analyzed, at different levels, using dynamic modeling in Vensim Software in the form of system dynamics analysis. The proposed model described industrial development, and using the simulation process, its behavior has been predicted in the next years. The model simulation results showed that the lack of development of large and water-intensive industrial projects in the region would cause the lowest amount of water resources loss in the Yazd-Ardakan Plain. They were determined as effective solutions to improve the resilience of water resources in the Yazd Ardakan plain.

Temperature is a significant atmospheric element that manifests climate change, specifically global warming resulting from an increase in greenhouse gas concentration. Atmospheric simulation is a critical tool in studying changes in atmospheric-climatic elements, particularly temperature.
The most commonly used tool for simulating the responses of the climate to greenhouse gas increases and examining future temperature changes is the use of climate variables simulated by coupled atmosphere-ocean models (AOGCMs). General circulation models (GCMs) are powerful tools aimed at generating climate scenarios. However, GCMs cannot provide effective information on climate simulation at local and regional scales. Therefore, the downscaling method is used to bridge the gap between local and global scales.
The current research aims to simulate maximum temperature using an artificial neural network model that adopts data from the atmospheric general circulation model (HadCM3) under RCP8.5, RCP4.5, and RCP2.6 scenarios for the Yazd synoptic station from 2006 to 2095. The independent variable, as the input to the artificial neural network, was selected for statistical downscaling using four statistical criteria: Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. Finally, the maximum temperature of the Yazd synoptic station for the next century was simulated.

The present study aims to investigate the maximum temperature of Yazd's synoptic station in the context of climate change based on valid scenarios until 2095. To achieve this, three sets of data were used: average daily maximum temperature data from Yazd's synoptic station, observed atmospheric data for the period of 1961 to 2005 (NCEP data), and simulated data from 2006 to 2095 based on release RCP scenarios. The NCEP data from 1961 to 2005 included 26 atmospheric variables that will be used as independent or predictor variables.
Modeling, simulating, and forecasting temperature based on nonlinear and chaotic time series is a challenging task. Prior studies have shown that artificial neural networks (ANNs) are suitable for simulating and predicting basic processes that are not well known. It is crucial to select the correct input variables intelligently and according to the purpose of the artificial neural network's design for prediction and simulation. Accordingly, in this study, the most suitable atmospheric parameters as the input of the artificial neural network were selected by pre-processing and selecting the atmospheric variables for the base period (1961-2005) to simulate with four statistical criteria (Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry). The resulting mean square error (MSE) obtained from the statistical criteria was compared, and the correlation coefficient and the similarity of the monthly time series trend of the simulated values with the target values were also analyzed. The best network architecture was selected to simulate the maximum temperature of Yazd's synoptic station from 2006 to 2095 under different RCP emission scenarios.

The selection of explanatory variables for downscaling was based on four statistical methods Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. After analyzing the mean square error (MSE), correlation coefficient, monthly average values of the maximum temperature of Yazd station, and estimated values from 1961 to 2005, the probability density function, cumulative probability function, and monthly time series trend obtained from all four methods, the explanatory variables were selected. These variables include mean sea level pressure, the divergence of 1000 hPa, zonal wind component, zonal wind intensity of 850 and 500 hPa, altitude and vorticity of 500 hPa, average temperature, and relative humidity at a 2 m height.
The structure and architecture of the neural network were designed based on these selected variables. The network consisted of a two-layer feedforward, with a sigmoid transfer function in the hidden layer, a linear function in the output layer, an input layer with eight variables, eight neurons, and the Lunberg-Marquardt training algorithm. This architecture was used to simulate the maximum temperature of Yazd's synoptic station under RCP2.6, RCP4.5, and RCP8.5 scenarios for two periods of 2050-2006 and 2095-2051.
Comparing the monthly average values of RCPs (RCP2.6, RCP4.5, and RCP8.5) in the first statistical period (2050-2006) with the base period (1961-2005), the maximum temperature of Yazd station indicates an increase in temperature in winter, spring, and summer, and a decrease in the autumn season under all three RCPs.
Comparing the monthly mean values of RCPs (RCP2.6, RCP4.5, and RCP8.5) of the second period (2051-1995) with measured mean maximum temperature (2005-1961) showed that temperature will increase the most in winter, spring, and summer, similar to the first period of the RCP8.5 scenario. In this scenario, unlike the other scenarios, the increase in temperature is evident in both subperiods for the autumn season. Finally, in the second period (2051-1995), the increase in the average maximum temperature of Yazd station in winter, spring, and summer, and the decrease in the average maximum temperature in autumn will be more significant.

The increase in greenhouse gas concentration resulting from human industrial activities is expected to cause global and regional warming in the future. The current study's findings indicate that the average maximum temperature of Yazd station will rise between 0.4 to 6.9  in winter, 0.2 to 8.1  in spring, and 1.1 to 7.7 in summer from 2006 to 2095. However, a decrease in the maximum temperature between 0.6 and 1.4  is expected in autumn. These results are consistent with those of other researchers.

The main purpose of this study was to mapping the canopy cover (CC) and the aboveground net primary production (ANPP) of plant life forms by the independent factor of physiographic in the rangelands of Ardabil province.Material &

First, according to plant types and environmental factors, sampling was taken at the peak stage of plant growth from 2016 to 2020 using one square meter plots. To determine the most effective environmental factor in the changes of plant parameters, the correlation between dependent and independent variables was used. Linear regression equation was used for modeling by the most effective factor and the maps were simulated. The accuracy of the equations was also calculated by RMSE. Then, the correlation between CC and ANPP of life forms and the total was obtained.

The results showed that the elevation factor had the highest correlation with the dependent variables. The difference between the estimated average of the model with the measured values for CC of grasses equal to +1%, forbs –1%, shrubs -1%, and total CC +2%; and these values for the ANPP of grasses equal to +21 kg/ha, forbs was equal to zero, shrubs were -13 and the total ANPP was +274 kg/ha.

Predicting the studied parameters will be necessary to manage the balance between supply and demand of rangeland products.

Severe climate changes (and global warming) in recent years have led to changes in weather patterns and the emergence of climate anomalies in most parts of the world. The process of climate change, especially temperature changes, is one of the most important challenges in the field of earth sciences and environmental sciences. Any change in the temperature characteristics, as one of the important climatic elements of any region, causes a change in the climatic structure of that region. The summary of the investigated experimental models on climate change shows that if the concentration of greenhouse gases increases in the same way, the average temperature of the earth will increase dangerously in the near future. More than 70% of the world's CO2 emissions are attributed to cities. It is expected that with the continuation of the urbanization process, the amount of greenhouse gases will increase. According to the fifth report of the International Panel on Climate Change, the average global temperature has increased by 0.85 degrees Celsius during 1880-2012. Therefore, knowing the temperature changes and trends in environmental planning based on the climate knowledge of each point and region seems essential. For this reason, the present study simulates the daily temperature (minimum, maximum and average) of Zanjan until the year 2100.

The method of conducting the research is descriptive-analytical and the method of collecting data is library (documents). To check the temperature of Zanjan city, the minimum, maximum and average daily temperature data from Hamdeed station of Zanjan city during the period of 1961-2021 were used. The data of general atmospheric circulation model was used to simulate climate variables (minimum, average and maximum temperature) using artificial neural network and climate scenarios in future periods. The output variables in this study are minimum, maximum and average daily temperature. Therefore, three neural network models were selected. For model simulation, model inputs (independent variables) need to be selected from among 26 atmospheric variables. Therefore, two methods of progressive and step-by-step elimination were chosen to determine the inputs of the model. In these methods, climate variables that have the highest correlation with minimum, maximum and average daily temperature were selected. By using RCP2.6, RCP4.5 and RCP8.5 scenarios, variables were simulated until the year 2100. Markov chain model was used to check the possibility of occurrence of extreme temperatures of the simulated values.

According to the RCP2.6, RCP4.5 and RCP8.5 scenarios and the simulation made by the neural network model, it is possible that on average the minimum temperature will be 3.6 degrees Celsius, the average temperature will be 3.3 degrees Celsius and the maximum temperature will be 2.7 degrees Celsius. Celsius will rise. The monthly review of the simulated data for all scenarios and the observed data of the studied variables shows that the average minimum, average and maximum temperatures in January and February, which are the coldest months of the year, will increase the most and become warmer. While the average minimum temperature in August, the average temperature in April and the maximum temperature in October will have the least increase. According to the simulated seasonal temperature table based on all scenarios, it was found that the average minimum, average and maximum temperature observed with the maximum simulated conditions were 6.9, 5.5 and 5.4 respectively in the winter season, and 3.3 in the spring season. 4, 2.3 and 3, in the summer season it increases by 3.3, 3.4 and 1.4 and in the autumn season it increases by 4.6, 4.5 and zero degrees. The frequency of extreme temperatures observed in all three variables of minimum, average and maximum temperature for the 25th and 75th quartiles is less than the number of occurrences of extreme temperatures simulated in all three scenarios. Based on this, all three variables will increase and there will be fewer cold periods. An increase in night temperature and average temperature in winter season and maximum temperature in summer season will occur more than other seasons. The difference between day and night temperature will be less in autumn and summer. Also, all seasons, especially the summer season, will be hotter and the occurrence of extreme temperatures is increasing for the coming years.

Along with the ever-increasing urban population, the amount of construction in the city space has been developed. The development of construction in the horizontal space and regardless of the existing restrictions has led to environmental, economic and legal problems for the citizens. Achieving the amount, intensity and direction of construction development from the past to the present and predicting the construction situation in the future is the first step towards the scientific and practical management of the physical development of urban construction, planning and providing suitable solutions in order to create a balance between allocation Spatial-spatial construction and all kinds of legal, economic and environmental considerations. Data and information extracted from satellite images, while showing the historical changes of urban construction, are used as the main, necessary and necessary input data for models to predict its future state. In this research, satellite images of TM, ETM+ and OLI sensors of Landsat satellite were used in the time periods of 1997-2007 and 2007-2017 related to the city of Sari. After performing geometrical corrections, city area maps were prepared. Then, by using the effective parameters in urban construction changes, using the Cellular Automata(CA) Markov Model, the accuracy of the simulations was checked. Finally, for validation, the simulated maps and the ground reality map were matched with each other. The simulation of the construction development process in 2027 using the CA-Markov model showed that if the existing management regulations continue, this area will decrease from 4617.90 hectares in 2017 to 4357.44 hectares in 2027. But the examination of change maps and stability maps showed that new areas will be under construction between 2017 and 2027, which were mainly used for agriculture and barren land.

Floods are occurring as an environmental hazard in many parts of the world, especially in countries that have been ineffective in crisis management and resource conservation. This natural hazard has become one of the most damaging natural disasters since the reckless activities of human beings in exploiting the natural environment have increased. One of the ways to deal with this danger is to predict the occurrence of floods. The models that are used to predict the occurrence of floods may not have the same accuracy in predicting precipitation events in different seasons of the year. Due to the importance of this issue, in this study, the results of the the Geomorphological Instantaneous Unit Hydrograph (GIUH) model in predicting floods in winter and spring in Minab catchment (Brentin) were compared. The data include the maximum hourly flow and their corresponding precipitation for three spring and three winter events. First, the required parameters of GIUH model including branching ratio, length ratio and area ratio for the basin were estimated using GIS softwar. Then, the events of winter and spring seasons, the peak discharge values, and the time of the peak discharge were compared. The results of this comparison showed that GIUH model predicts peak discharge and peak flood time occurring in winter (with average values of 20.09 and 45.64%, respectively). It is less distant from the observed data, and has a higher accuracy than spring events (with average values of 57.3 and 119.7, respectively).

In this study, we analyzed the spatial–temporal trends of land use dynamics from 2000 to 2021 using remote sensing data. The image classification was based on three main land use classes, i.e. forest, artificial areas (agriculture and built-up), and other natural areas (bare lands, grassland, plantation, shrubland, water bodies, and woodlands). Maps of land use changes in the area for 2000-2021 show that built-up areas have increased by 9.3%. In contrast, forest and other natural areas decreased by 7.1% and 2.2%, respectively. In addition, an integrated CA-ANN (Artificial Neural Networks- Cellular Automata) model was used to predict land use changes from 2021–2042. The percentage of correctness for the simulation was 91%, and the overall kappa value was 0.86. Similar to the classified maps in 2000-2021, the prediction maps from 2021–2042 illustrated increasing trends in built-up areas by 4.7% and decreasing trends in the forest by 4.26% and other natural areas by 0.4%. In this work, we implemented ordinary least squares (OLS) regression models to predict land cover changes in the study area as a function of explanatory variables [elevation, slope, and proximity variables - distance to the city center, roads, villages, and streams-]. The results of the OLS models showed a relatively good performance for predicting land use changes with an R-squared value greater than 0.5. These results provide important knowledge that can help develop future sustainable management and planning and help managers make informed decisions to improve environmental and ecological conditions.

Review

The aim of this study was to evaluate and identify the trend of historical patterns of urban growth, simulate and predict the size and future growth of Zahedan city, and seek to investigate and identify the intervening factors in determining the intelligent direction of urban areas and the intention to answer this key question whether the growth of Zahedan urban area is in accordance with the prepared and planned patterns and matched with the population. Results/

The results showed that the historical growth trend of Zahedan city will be mostly based on organic growth, growth based on road orientation and urban areas growth as edge and future growth of the city internally and then on the edges of the existing texture. The results of the future growth of Zahedan city showed that further growth will be toward the west and parts of the south, which considering the desirability of land development in the western half, makes the pattern and the growth rate of the range in the future logical and appropriate.

In this research, in order to simulate the underground water level of Khorramabad plain, the performance of hybrid models of bat support vector regression, bat support vector regression, gray wolf support vector regression for four piezometric wells of Sarab Pardah, Naservand, Sally and Baba Hossein Bridge, which have homogeneous statistics. and it was done without missing data. For modeling, rainfall (P), temperature (T) and underground water level (H) and withdrawal from water resources (q) have been used in the monthly report of the models during the period of 1380-1400. It should be noted that for modeling, 80% of the data is chosen for training and the remaining 20% ​​for testing, randomly, which covers a wide range of data types. Correlation coefficient (R), root mean square error (RMSE), mean absolute value of error (MAE) and Sutcliffe coefficient of vitality (NS) were used to evaluate and compare the performance of the models. The results showed that the combined structure provides better performance than other structures in all the investigated models. Also, the results showed that the wavelet support vector regression model based on the evaluation indicators, in the piezometric well of Sarab Pardah has R=0.978, RMSE=0.221 m, MAE=0.011 m, NS=0.985 and also in the piezometric well of Naservand has R=0.981 . 010 m, NS=0.986 and finally piezometric well, Baba Hossein Bridge has 5 R=0.98, RMSE=0.101 m, MAE=0.007 m, NS=0.995, compared to other models, it can be used to create a favorable result.

The aim of the above research is to simulate the changes of Balikhlichai river using CAESAR model and HEC-RAS hydraulic model in Ardabil province.The results of the HEC-RAS hydrological model showed that the maximum flood area in the return periods of 2000 and 10000 years is 237.2 and 242.2 hectares, respectively, and the probability of occurrence is 99.95 and 99.99 percent, and the width is 1170, respectively. and it is 1288 meters, which will be dangerous for human societies and human facilities.. The results of the cellular automatism model also showed that the morphological changes of the river bed in the sections of width No. 1 in the first interval were equal to 0.5 meters. Also, the changes in the river bed in the second period of the 28 sections showed 1 meter and 30 cm, respectively, in which the minimum and maximum amount of changes can be seen. The changes in the river bed in the third period were about 1.5 meters and the changes in the eighth period were about 1.5 meters. Therefore, it can be concluded that according to the results, the maximum amount of changes in the river bed of Balikhlichai occurred in the third and fourth period near the residential areas of Ardabil province, because human interference has led to the rapid growth of erosion and changes in the river bed. Finally, it is suggested that in future studies, the influence of human societies in creating and increasing the amount of riverbed changes and increasing erosion and sedimentation should be investigated, and preventive measures such as reducing human interference in the riverbed, not encroaching on the river boundary, and preventing land use changes should be carried out. and this will lead to the normal movement of the river in its bed.

The wind has always been considered an energy source from two perspectives: pattern and behavior in urban contexts and potential in suburban environments. There are usually two major strategies for this

one based on observational data and the other providing simulation data with the creation of climate models at various numerical scales (Han et al., 2014: 17). Numerical models are used in most studies to evaluate regional winds nowadays (Haman et al., 2010: 954; Shimada et al., 2011: 21). Simulated weather research and forecasting (WRF) has been used to conduct studies on this topic (Liu et al., 582: 2018; Salvaso et al., 276: 2018; Matar et al., 22: 2016; Charabi et al., 1: 2019; Tokhtenhagen et al., 119: 2020). The sensitivity and performance of the WRF model to initial and boundary conditions, as well as its impact on wind simulation, are investigated in this study. A planetary boundary layer scheme is also chosen to simulate the wind field in the city of Tehran.

The Meteorological Organization provided observational data on wind direction and speed for Mehrabad, Chitgar, Geophysical, and North Tehran (Shemiran) synoptic stations from 2018 on a three-hour time scale (Table 2). Data analysis time series from two databases, the National Environmental Forecasting Center (NCEP-FNL) and the European Center for Medium-Term Weather Forecasting (ECMWF-) ERA5), were used as the initial and boundary conditions to achieve the frequency and distribution of wind direction and velocity for January, May, July, and October. The WRF model, version 4.1.2, was used to simulate the components of wind speed and direction using boundary condition data in this investigation. The RRTM longwave radiation model, the Goddard shortwave radiation design, the Noah surface model, the WSM6 microphysical schema, the two-dimensional Cumulus Betts-Miller-Janjic schema, and the three-dimensional Grell-Freitas schema were all employed in the study. The MRF Medium-Range Prediction Model, the Younesi University YSU Scheme, the MYJ Scheme, the second ACM2 Asymmetric Convection Scheme, the QNSE Normal Gaussian Scale, and the second and third MYNN Turbulence Scale are all used to test the performance sensitivity of the planetary boundary layer schemas.

By checking the characteristics of the observation stations according to table 9, all the selected stations have an average height difference of at least 110 meters, and the difference between the lowest (Mehrabad) and the highest (Shimiran) station is 360 meters. According to the results from the selected stations, this feature can be effective in the accuracy of the simulations by the weather prediction research model. It can be stated that the model cannot correctly simulate the topography due to the low horizontal resolution in the inner domain (7 km) and static data (such as DEM and land cover (by default, these data in the model have a horizontal resolution of approximately 1 km)) to do Therefore, it is not possible to establish a meaningful relationship between the height difference of the stations and the output of the model. Still, the lack of proper introduction of the elevations of the land to the model causes the performance of the model to be weak so that it can simulate the surface currents resulting from local factors correctly.

According to the analyzes done with wind and statistics, it seems that the weather research and forecasting model is more weak in estimating the wind direction in the months when the average monthly wind speed is lower, and it can be said that in the months of July and October, the wind is generally controlled by local factors with Low speed is formed, on the other hand, due to static data with low spatial resolution, the morphology and morphology of the model is weak and due to the dependence of surface currents on topography, it causes a large error in the estimation of the wind direction by the model in the mentioned months, but this weakness in The cold months decrease with the passage of dynamic systems and the increase of the monthly average wind speed, but contrary to the wind direction, the wind speed estimation outputs by the model show that the increase of the monthly average wind speed causes a decrease in the accuracy of the model in the estimation of the wind speed variable, that is why in all the statistics, July has the best simulation in wind speed variable. From the results of these studies, the selected configuration for the direction may not necessarily be associated with the desired results for the speed. It may even be possible to achieve the best output in the months of the year with different configurations. According to the selected boundary configurations and data, the results of this study seem to be consistent with the research of Santos et al. (2013), Gholami et al., Ghafarian et al. (2018), and Laighi et al. (2015) are confirmed.

Buildings are one of the main pillars of economic and social development of countries that consume a large part of energy and natural resources. The purpose of writing this article is to calculate the energy consumption of a building in one year, determine the energy label and then optimize some of its features in order to reduce construction costs and reduce energy consumption of the operation phase. Is. The study area is common residential buildings in Tehran. The research method to achieve this goal is as follows: First, according to the licensing statistics of Tehran Municipality, a 5-storey southern building was selected on a land with an area of ​​320 square meters and an infrastructure of about 1100 square meters, which represents a large number of buildings in Tehran. Be. Then, by simulating the building considered in Builder Design software, the amount of gas consumption is 145.53 kWh per square meter, electricity consumption is 81.25 kWh per square meter and a total of 226.79 kWh per meter. The square was calculated per year. With this consumption, according to standard 14253, the building receives energy label C. Then, with the aim of reducing the cost of construction and energy consumption during operation, it was optimized with a genetic algorithm. Research variables, type of exterior wall (pottery or Leica), exterior (stone or brick), type of window glass (plain or low emission), type of gas between the layers of window glass (air or argon) and the ratio of the window to the south front surface (15 % -30% -45% -60%) were built. The results indicate that the best possible scenarios for the exterior facade, stone, low-emission double-glazed windows with argon gas, the outer wall of the pottery with a window-to-south front ratio of 22.5% or Leica, with a window ratio At the level of the southern front, it is 37.5%.

Simulation of the rainfall-runoff process in a watershed is highly important from the point of view of hydrological issues, water resources management, river engineering, flood control structures and its storage. Rainfall-runoff estimation using a distributed hydrological model and the technique of GIS has become possible, practical and common. The Wetspa model is a distributed model simulating runoff and water balance that is performed at different time scales including hourly or daily basis. In this research, the discharge is simulated using Wetspa model in the Ghorichay watershed. This watershed (as one of the sub-watersheds of Gorganrood) with an area of 2481.5 ha is located in the south of Ramian city in Golestan province. The data used by the model are land use maps, soil texture, digital elevation model, precipitation, evaporation, temperature and discharge (for calibration and validation of the model). Calibration of 13 parameters was performed manually and automatically for 7 years at the beginning of the statistical period and model validation was performed for a period of 5 years. The results of the model evaluation show the accuracy of discharge simulation and very good agreement between the simulated data and observations based on the Nash-Sutcliffe criterion of 67.21% in the calibration period and 76.34% in the validation period. Also, the discharge calculated by the Wetspa model for the whole watershed was 28.31%, which in comparison with the observed discharge of 30.12% indicates a good simulation of the model.

Among all the natural hazards in the country, according to recorded statistics and observations, floods have been the most destructive, and it has the highest frequency of occurrence. Floods are one of the known natural disasters that cause a lot of financial and human losses. This phenomenon can be controlled by identifying flood-prone areas and proper management. In the current era, due to human encroachment on rivers and land-use change or destruction of vegetation, flood damage has increased. These factors cause, in addition to increasing human and financial losses, damages such as soil erosion upstream and sedimentation downstream. In this research, using agent-based modeling in the NetLogo simulation environment, flood-prone areas in Shush city have been identified. The most important input was topography (digital elevation model) and then dynamic and temporal simulation was done by performing tessellation on the area and considering the rainfall in each cell as an agent. Using spatial analysis in ArcGIS software and comparing the simulation results with the location of the city and land use maps of the region, the possible causes of floods in this region have been investigated. Agent-based models with the incorporation of geospatial information systems (GIS) can be used as a new solution to solve spatial problems such as natural crises, destructive environmental impacts, and so on. Finally, preventive measures to prevent floods in this area are proposed.

Severe soil erosion is a serious threat to the sustainable management of land and the use of water and soil resources in many parts of the world. In order to control erosion of sheet, rill, gully, and stream bank erosions and to reduce the resulting sediment at the outlet of watersheds, it is necessary to identify the share of sources that produce their sediment to make protective measures more successful. One of the most common methods that has been used in recent years to determine the share of different sources of sediment is the sediment fingerprinting method.

The purpose of this study is to investigate contribution of sheet, rill, gully and stream bank erosions in sediment production by using sediment fingerprinting method in Neyriz watershed, located in East of Fars province, with the help of sampling of sediment deposited in the bed. From each type of sediments, sheet, rill, gully and stream bank erosions, the main waterway within the basin and the outlet area of ​​the watershed, 10 samples (60 samples in total) were collected. In order to determine the optimal tracers, two tests of "domain" test and "multivariate detection analysis" were used. Furthermore, by using the model of Collins et al., the share of each of the different sources of sediment was obtained. Then, the uncertainty related to the share of potential sources of sediments was calculated using the Monte Carlo simulation method with 95% confidence in MATLAB software. In order to evaluate the results of the hybrid multivariate model, the Goodness of Fit (GOF) proposed by Collins et al. was used.

Based on the range test, among the 51 tracers measured in the samples, twelve tracers (Ag, Ba, Be, Eu, Mn, Ni, Ta, Tb, Th, Tm, W, and Zn) are found as tracer’s non-conservative variables were identified, and these detectors were discarded in other statistical tests such as Kruskal-Wallis and discriminant analysis function. The results of the Kruskal-Wallis test showed that among the 39 tracers that passed the range test, sixteen tracers (Al, Ca, Co, Cr, Er, Fe, Gd, Lu, Mo, Na, Nd, Pb, Pr, S , Sc and Zr) with significance at one percent level (p ≤ 0.01), and 9 tracers (Cu, Ga, Hf, Ho, La, Sn, Sr, Y and Yb) with significance at five percent level (p ≤ 0.05) is that in total, these 25 detectors had a significant level and could separate sources; while fourteen tracers (As, Ce, Cs, Dy, K, Li, Mg, Nb, P, Rb, Sm, Te, Ti and V) were not statistically significant, these tracers were deleted from the DFA statistical test. In the first step of the DFA test, the Zr detector, the second step of Zr and Al detectors (with Wilkes lambda from 0.717 to 0.244), the third step of Al, Zr and Fe detectors (with Wilks lambda from 0.39 to 0.057), the fourth step of Zr, Al, Fe and Sn detectors (with Wilkes-lambda 0.362 to 0.04), the fifth step of Zr, Al, Fe, Sn and Lu detectors (with Wilkes-lambda 0.233 to 0.03) and the sixth step Zr, Al, Sn and Lu tracers (with Wilks lambda 0.289 to 0.045) were entered into the model. Based on the obtained results, among the 25 tracers that passed the Kruskal-Wallis test, five tracers (Al, Fe, Lu, Sn and Zr) were entered into the DFA test step by step. In the third stage, iron tracer (Fe) was added to the model and in the sixth stage, it was removed from the DFA test. In general, four Zr, Al, Sn and Lu tracers were selected as the final optimum tracers. These four detectors were able to correctly classify 95% of sediment sources. The findings of this research, which were obtained by using Monte Carlo simulation and the combined multivariable model and evaluating their results using GOF, showed the contribution of gully, sheet, rill and stream bank erosion to the order is equal to 45.21, 3.07, 16 and 35.72% of the total erosions that have occurred in this watershed. Also, considering the GOF value of 0.8869 and mentioning that the closer this value is to one, the more accurate the results of the model is true in this research and this analysis also confirms the high accuracy of the model.

In this study, the efficiency of sediment fingerprinting method was proved as a successful and effective method to determine sediment sources because the first and most important stage of the sediment source method is to choose a suitable combination of tracers that can isolate sediment sources, and this was done correctly in this research. Also, Monte Carlo uncertainty confidence levels showed that the scope of this uncertainty is large (0.8869) and therefore, it shows a greater lack of certainty on different sources of sediment production. Determining the share of four types of erosion in the Neyriz watershed and placing the share of gully erosion as the most important type of erosion in the production of productive sediments in it shows the importance of controlling erosions, especially gully erosion, with emphasis on biological plans.