فهرست مطالب محمد انصاری قوجقار

Nowadays, due to the increase in population and the ever-increasing need for food, providing food security is considered one of the essential requirements in every region. On the other hand, the recent droughts are one of the main reasons for the instability in food security and food production in different regions of the world. The purpose of this research is to select a superior model in order to measure the relationship between drought parameters and food security in Alborz province. Therefore, from the data related to precipitation and temperature in order to calculate SPEI meteorological drought index, as well as the data related to the cultivated area and tonnage (tons per hectare) of the two strategic products of wheat and barley, which provide more than 43% of the province's food. It was used in a period of 21 years (1380-1400). Four methods of Spearman, ANN, ANFIS and M5 were used to investigate the relationship between drought and food security parameters in Alborz province. The results showed that in Taleghan and Karaj, the goodness of fit criteria of R2, RMSE and MAE of Spearman, ANN, ANFIS and M5 methods were from 0.752 to 0.736, 0.242 to 0.217, 0.107 to 114 respectively. 0.847 to 0.837, 0.209 to 0.253, and 0.107 to 0.111 are variable. Also, among the algorithms used, the M5 decision tree method was the best model for measuring the correlation between drought variables and food security...

The purpose of this study is to present a comprehensive and analytical framework of a type of governance that, in addition to updating and making the components of governance more intelligent, will increase social participation in all fields, especially in the issue of environmental geopolitics. In order to achieve the aforementioned goal, this research was conducted by reviewing the studies conducted in the fields of governance and metaverse, using the descriptive-analytical method and citing library sources. In this research, while asking why the movement from the third type to the first type of governance coordinates, as well as the need to update the components of the first quadrant of governance coordinates, using the emerging metaverse industry, the concept of metaverse governance is explained and its analytical framework in order to reach a way of governance. Community-oriented - result-oriented - native-oriented, it is argued. Then, participatory management derived from the perspective of metaverse governance is discussed and the methods of applying this type of management in solving environmental geopolitical issues are discussed. In general, the use of the metaverse industry in educational and interactive structures and its effect on the application of participatory management in the field of environmental geopolitics provides the possibility for the leaders of the country's environmental governance to change the paradigm in the way of governance from a government-centered to a community-centered type. Accelerate and change the country's environmental governance pillars from the third quadrant of coordinates to the first quadrant of governance types.

Wheat as the main food in the country is of particular importance. Wheat is not only an important economic agricultural commodity in the world, but also known as a powerful lever in political and global relations. Therefore, the analysis and forecast of the production status of this product in the country has always been the focus of attention. The purpose of this study is to predict the amount of wheat yield (X) using artificial intelligence in the annual time scale in Alborz province. For this purpose, using annual cultivation and production data, wheat yield was investigated in six cities of Nazarabad, Savojbalagh, Karaj, Eshtehard, Fardis and Taleghan with a period of 40 years (1981-2020). After calculating the yield (ton per hectare) and forming an annual time series, four artificial intelligence methods including the best neighbor algorithm (KNN), backup vector (SVM), gene expression planning (GEP) and Bayesin Network (BN) were used and the wheat yield was predicted for the following year. Results indicated a more precision in yield prediction in the years with more production; According to the results of the BN, GEP, SVM and KNN model, the correlation coefficient between the observed and anticipated wheat yield values was 0.84, 0.89, 0.89 and 0.92, respectively. Explaining that Karaj and Taleghan cities have the highest and lowest wheat production respectively. The results showed that the KNN method had the best accuracy among the others, as the values of R, RMSE and MAE varied from 0.84 to 0.92, 0.21 to 0/24 and 0.11 to 0.18. Overall, by comparing the proposed methods, the KNN method had the highest and the BN method had the least accuracy to predict the amount of wheat yield in Alborz province. The results of this study can be very useful in providing and managing food security in areas under study.

The impact of dust phenomenon in Iran is so vast that it has involved more than half of the country's provinces in some way with the issues and limitations of this natural phenomenon. In addition to the environmental effects, it has disrupted the implementation of national sustainable development plans and so far, it has had many negative consequences. This research tries to present a new hybrid model using artificial intelligence hybrid metamodels as well as Box Jenkins hybrid metamodels to predict and model the FDSD index (frequency of days with dust storms), in seven synoptic stations of Khuzestan province with length The statistical period has been 40 years (1981-2020). The hybrid prediction algorithms used in this research include W-ANFIS, AF-SVM, ARIMA-NARX and SARIMA-SETAR. The prediction results showed that the decrease in the performance of hybrid models to predict the FDSD index has a direct relationship with the decrease in the frequency of days with dust storms. So that the correlation coefficient for experimental data in AF-SVM and W-ANFIS hypermodels from 0.991 and 0.985 to 0.985 and 0.958, respectively, and Nash Sutcliffe coefficient has also decreased from 0.977 and 0.960 to 0.973 and 0.952, respectively. Also, the RMSE coefficient from Abadan station to Dezful for the two metamodels from 0.135 and 0.151 to 0.140 and 0.179 respectively, And the MAE coefficient has also increased from 0.054 and 0.068 to 0.060 and 0.093, respectively. Correlation coefficient for test data in Box Jenkins SARIMA-SETAR and ARIMA-NARX hypermodels also from 0.967 and 0.951 to 0.958 and 0.941 respectively and the Nash Sutcliffe coefficient has also decreased from 0.945 and 0.923 to 0.938 and 0.913, respectively, which indicates the weakening of the performance of hybrid metamodels with the decrease in the frequency of dust storms in Khuzestan province. Also, by fitting four hybrid hypermodels on the FDSD index, it was shown that AF-SVM hybrid hypermodel had better performance than other methods. In a way, in all studied stations, the correlation coefficient and Nash-Sutcliffe coefficient are higher and the root mean square error coefficient and the mean absolute value of the error are lower, which shows the superiority of this hybrid meta-model over other meta-models for predicting the FDSD index in Khuzestan province. The results of this study can be used to model dust storms in other western regions of the country.

In this study, the flow inside a rectangular composite channel was studied by finite volume numerical method, for which Flow-3d software was used. Comparing the results of the numerical model with the laboratory results, it was concluded that the numerical model can well simulate the flow inside a rectangular composite channel. Performing numerical modeling of suitable boundary conditions, a grid with the desired dimensions and a suitable turbulence model was obtained to describe the hydraulic flow inside the rectangular composite channel. Three turbulence models of the two equations K-ε, RNG K-ε and K-ω and one turbulence model of large vortices LES were used. Also, the effect of geometric parameters and flow on the average velocity in the composite channel was investigated. The results obtained from this study show that with increasing the width of the floodplain at different depths, the flow velocity has decreased, so that in the value of = 0.75 (yf / H), with decreasing the flood width of the right plain from 5.71 to 13.33 cm, the ratio of flow velocity in the section of the main channel to the average velocity has also increased by 51%. It should be noted that with increasing the depth of the floodplain, the flow velocity has decreased. Also, with increasing the height of the main channel wall, the flow velocity decreases.

. In this study to predict dust storms, hourly dust data and monthly data maximum, minimum, average temperature, maximum wind speed, and total precipitation in three synoptic stations of Abadan, Ahvaz, and Bostan with statistics period for 25 years (1990-2014) were collected. To investigate the impact of dust storms from climatic fluctuations, in addition to the mentioned variables, the Standardized Precipitation Evapotranspiration Index (SPEI) was also calculated in the seasonal time window. Predicting the number of days with seasonal dust storms using four artificial intelligence methods including MLP, ANFIS, RBF, and GRNN was performed. These were evaluated in the form of three experiments including the effect of adding auxiliary features on the prediction, the effect of the number of previous seasons on the prediction, and the best technique among the models used. The results showed that in all stations, the use of all features has improved dust prediction and the value of the Mean Absolute Error (MAE) for Abadan, Ahvaz, and Bostan stations is equal to 1.15, 1.66, and 0.66, respectively were obtained, all of which were related to the autumn season. In conclusion, it can be said that in Bostan station, if all the features and data of the last four seasons are used, the ANFIS model as input causes the prediction error to be reduced and a better result to be obtained. In the Abadan station, using the MLP model gives such a result.

Scouring is a natural phenomenon that occurs as a result of the erosive factor of water flow. Due to the impact of water on the base of the pier, vortices called horseshoe vortices are formed. Horseshoe vortices are more active in front of the base, which is the main cause of scouring. In this study, the effect of using triangular structures on reducing scour around the pier bases was investigated in a laboratory. This research was used in a laboratory flume with a 90-degree arc. Triangular protective structures with angles of the 15, 30, 45 and 60 degrees and a curved triangular structure were considered. Experiments were performed at the 6, 7.5, 8.5 and 10 liters per second. The results showed that the installation of triangular protective structures at a maximum flow rate of the 10 liters per second, can reduce the scour depth flow up to 84 and 78 percent, respectively, upstream and downstream. The best performance is for the 15° upstream protection structure as well as the 60° downstream protection structure. The results show that the scour depth increases with increasing landing number. Also, the curved protective structure had a benign performance current upstream and a lower performance current downstream than other protective structures.

Various factors play a role in climate change, including oceanic processes (such as ocean orbits), changes in solar radiation received from the earth, volcanic eruptions, and changes in human activity. Climate change has affected atmospheric and oceanic phenomena such as the Earth's surface temperature, precipitation, sea level, wind speed, wave height, coastlines and other phenomena and has changed these characteristics. In studying the effects of climate change on oceanic phenomena, estimating the long-term impact of climate change is of great importance. This is important because the offshore structures in these areas are affected by these changes and there is a need to amend the standard guidelines for the design of coastal structures. In this study, using the CGCM3 climate change model, wind speed values ​​for the Persian Gulf and the Sea of ​​Oman were extracted. These values ​​as model input, using the third generation numerical model MIKE21 (SW) which simulates wave parameters with the least assumptions, were converted into wave characteristics and finally the amount of changes in wave characteristics such as wave height Wave period and wave direction have been obtained in the Persian Gulf, the Sea of ​​Oman and part of the Indian Ocean that is affected by climate change in the region. The results of studies have shown that the effect of climate change on wave height and wave period is negligible, but in Makran beaches the effect of climate change on the angle of the wave direction can be considered and this will affect the sedimentation process. The effect of climate change on the wave direction and its effect on the structure should be further investigated for the construction of ports and marine structures such as desalination catchments in these areas.Various factors play a role in climate change, including oceanic processes (such as ocean orbits), changes in solar radiation received from the earth, volcanic eruptions, and changes in human activity. Climate change has affected atmospheric and oceanic phenomena such as the Earth's surface temperature, precipitation, sea level, wind speed, wave height, coastlines and other phenomena and has changed these characteristics. In studying the effects of climate change on oceanic phenomena, estimating the long-term impact of climate change is of great importance. This is important because the offshore structures in these areas are affected by these changes and there is a need to amend the standard guidelines for the design of coastal structures. In this study, using the CGCM3 climate change model, wind speed values ​​for the Persian Gulf and the Sea of ​​Oman were extracted. These values ​​as model input, using the third generation numerical model MIKE21 (SW) which simulates wave parameters with the least assumptions, were converted into wave characteristics and finally the amount of changes in wave characteristics such as wave height Wave period and wave direction have been obtained in the Persian Gulf, the Sea of ​​Oman and part of the Indian Ocean that is affected by climate change in the region. The results of studies have shown that the effect of climate change on wave height and wave period is negligible, but in Makran beaches the effect of climate change on the angle of the wave direction can be considered and this will affect the sedimentation process. The effect of climate change on the wave direction and its effect on the structure should be further investigated for the construction of ports and marine structures such as desalination catchments in these areas.Various factors play a role in climate change, including oceanic processes (such as ocean orbits), changes in solar radiation received from the earth, volcanic eruptions, and changes in human activity. Climate change has affected atmospheric and oceanic phenomena such as the Earth's surface temperature, precipitation, sea level, wind speed, wave height, coastlines and other phenomena and has changed these characteristics. In studying the effects of climate change on oceanic phenomena, estimating the long-term impact of climate change is of great importance. This is important because the offshore structures in these areas are affected by these changes and there is a need to amend the standard guidelines for the design of coastal structures. In this study, using the CGCM3 climate change model, wind speed values ​​for the Persian Gulf and the Sea of ​​Oman were extracted. These values ​​as model input, using the third generation numerical model MIKE21 (SW) which simulates wave parameters with the least assumptions, were converted into wave characteristics and finally the amount of changes in wave characteristics such as wave height Wave period and wave direction have been obtained in the Persian Gulf, the Sea of ​​Oman and part of the Indian Ocean that is affected by climate change in the region. The results of studies have shown that the effect of climate change on wave height and wave period is negligible, but in Makran beaches the effect of climate change on the angle of the wave direction can be considered and this will affect the sedimentation process. The effect of climate change on the wave direction and its effect on the structure should be further investigated for the construction of ports and marine structures such as desalination catchments in these areas.

With the growth of urban population, due to the increase in air pollution and also reaching the desired per capita green space, the need for urban green space development becomes clearer than before. However, the scarcity of extractable water resources raises concerns in this area. Water resources management is the only appropriate and principled solution to compensate for a large part of the water shortage required by green space. Calculating and estimating the water demand of urban green space can be a great help in planning for the optimal use of water resources. In this study, the evapotranspiration of urban green space plants in area one of Tehran Municipality with a net area of ​​1535.4 hectares was estimated using three methods WUCOLS, IPOS and PF. The study area includes 52 different plant species. To calculate the reference evapotranspiration and the amount of effective rainfall, climatic data of the North Tehran Meteorological Station for a period of 30 years (1365-1396) were used. The results showed that the highest amount of calculated water requirement is related to the PF method at the rate of 786.72 mm per year and the lowest is related to the IPOS method at the rate of 378.08 mm per year. Also, the results of this study indicate that the WUCOLS method with a total amount of calculated water requirement of 771.33 mm per year, due to having more and more complete parameters and more appropriate relationship with all objectives in urban green space, a method It is more suitable for estimating the water demand of urban green space.With the growth of urban population, due to the increase in air pollution and also reaching the desired per capita green space, the need for urban green space development becomes clearer than before. However, the scarcity of extractable water resources raises concerns in this area. Water resources management is the only appropriate and principled solution to compensate for a large part of the water shortage required by green space. Calculating and estimating the water demand of urban green space can be a great help in planning for the optimal use of water resources. In this study, the evapotranspiration of urban green space plants in area one of Tehran Municipality with a net area of ​​1535.4 hectares was estimated using three methods WUCOLS, IPOS and PF. The study area includes 52 different plant species. To calculate the reference evapotranspiration and the amount of effective rainfall, climatic data of the North Tehran Meteorological Station for a period of 30 years (1365-1396) were used. The results showed that the highest amount of calculated water requirement is related to the PF method at the rate of 786.72 mm per year and the lowest is related to the IPOS method at the rate of 378.08 mm per year. Also, the results of this study indicate that the WUCOLS method with a total amount of calculated water requirement of 771.33 mm per year, due to having more and more complete parameters and more appropriate relationship with all objectives in urban green space, a method It is more suitable for estimating the water demand of urban green space.With the growth of urban population, due to the increase in air pollution and also reaching the desired per capita green space, the need for urban green space development becomes clearer than before. However, the scarcity of extractable water resources raises concerns in this area. Water resources management is the only appropriate and principled solution to compensate for a large part of the water shortage required by green space. Calculating and estimating the water demand of urban green space can be a great help in planning for the optimal use of water resources. In this study, the evapotranspiration of urban green space plants in area one of Tehran Municipality with a net area of ​​1535.4 hectares was estimated using three methods WUCOLS, IPOS and PF. The study area includes 52 different plant species. To calculate the reference evapotranspiration and the amount of effective rainfall, climatic data of the North Tehran Meteorological Station for a period of 30 years (1365-1396) were used. The results showed that the highest amount of calculated water requirement is related to the PF method at the rate of 786.72 mm per year and the lowest is related to the IPOS method at the rate of 378.08 mm per year. Also, the results of this study indicate that the WUCOLS method with a total amount of calculated water requirement of 771.33 mm per year, due to having more and more complete parameters and more appropriate relationship with all objectives in urban green space, a method It is more suitable for estimating the water demand of urban green space.

It is clear that the ENSO phenomenon affects the hydrological and climatic regimes in different parts of the world, but the extent of this effect in different parts of the world has not yet been answered. Therefore, this research has been done to answer this important question. In this research, using the Oceanic Niño Index (ONI), the effect of the positive phase of the El Niño-Southern Oscillation (ENSO) on the Frequency of Dust Stormy Days (FDSD) in 12 synoptic stations located in Khuzestan and Sistan and Baluchestan provinces over a period of 40 years (2019-1980) has been reviewed. For this purpose, hourly dust data, codes of the World Meteorological Organization, Adaptive Neural-Fuzzy Inference System (ANFIS) and time changes of FDSD index in two neutral phases and the occurrence of El Niño were used. The results of ANFIS model estimation and observational values of FDSD index showed that at the occurrence time of El Niño in Khuzestan and Sistan and Baluchestan provinces, equal to 33 and 17 events, respectively, the observable values of the frequency of days with dust storm were less than the estimated values. The results also showed that the positive phase of ONI is more effective on dust storms in Khuzestan province than in Sistan and Baluchestan province. Therefore, during the hot phase of ENSO, more measures should be taken to control and manage dust storms and their destructive effects in areas where the source of dust storms is external.

The operation of reservoir dams has so far been done with emphasis on the goals of meeting water needs and controlling flood energy and improving environmental conditions, and less attention has been paid to the issue of sediment removal from the reservoir of the dam. In this study, a semi-cylindrical structure in front of the lower discharge was tested to determine a method to improve sediment discharge. The experiments were performed by constructing 16 physical models with different lengths and widths of gaps in four water heights, three half-cylinder diameters and a total of 208 experiments. The results of the experiments showed that by placing a semi-cylindrical structure in front of the lower discharge, the volume of leaching sediment increases. In other words, a pair of rotating vortices is created inside the semi-cylindrical structure on both sides of the central axis of the valve, in which case the installation of a half-cylinder with different diameters increases the volume and length of scale washing compared to the model without a cylinder. . So that by increasing the height of the gap four times, the volume and length of sedimentation increase by 101.3 and 27.2 percent. The results of this study showed that the installation of semi-cylinders with diameters of 12.7, 15.2 and 19.2 on average will result in an average of 587, 732 and 829 percent increase in sediment volume compared to the model without cylinders. Also, installing semi-cylinders with diameters of 12.7, 15.2 and 19.2 on average will increase the length of scouring by 94, 106 and 111 percent, respectively, compared to the model without cylinders

Understanding the frequency of dust storms in each area and being mindful of temporal-spatial variation of this event can help to monitor and reduce the damages induced by dust events. Due to the increasing development of metamodels and their combination with optimization algorithms used to model and predict hydrological variables, machine learning models due to high accuracy in forecasting, in the form of a black box, have received a lot of attention. Therefore, in the present study, a hybrid approach is proposed to predict the Frequency of Dust Storm Days (FDSD) on a seasonal scale, which uses a combination of Lang Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks. In this study, the performance of the proposed hybrid model was compared with a neural network based on Radial Basis Functions (RBF) and Support Vector Machine (SVM). For this purpose, hourly dust data and codes of the World Meteorological Organization were used on a seasonal scale with a statistical period of 30 years (1990-2019) for seven synoptic stations in Khuzestan province. The results of the evaluation criteria in the training and testing Stages showed that the GRU-LSTM hybrid model offered better performance than other models used to predict the frequency of days with dust storms; The proposed hybrid model with correlation coefficient (R) of 0.905-0.988, Root Mean Square Error (RMSE) of 0.313-0.402 day, Mean Absolute Error (MAE) of 0.144-0.236 day, and Nash-Sutcliffe (NS) of 0.819-0.903 had better performance compared to the other models used in predicting the FDSD index. In general, comparing the models used in this study, the GRU-LSTM hybrid method and the SVM model, respectively, provided the best prediction skills. As a result, application of the proposed hybrid model can be used as a suitable tool to predict the FDSD index and adopting management decisions to reduce the dust storms damages in the study area.

The increase of dust storms occurrence in recent years in western and southwestern Iran has doubled the importance of prediction and communication of this phenomenon with climate variations. Analyzing and identifying of dust storms and its association with climatic parameters is one of the crucial approaches to reduce the caused damage of this phenomenon. Since besides determining the portion of each climate variables in intensifying the circumstances, it also can play a fundamental role in priorities, macro management policies, and upstream rules in order to control and prevent dust particles. The purpose of this study is to model the relationship of Frequency of Dust Stormy Days (FDSD) with extreme and average temperature variables in the western half of the country.

For this purpose, the hourly data of dust and codes of the World Meteorological Organization, as well as climatic data including average temperature, maximum temperature and minimum temperature on a monthly scale with a statistical period of 25 years (1990-2014) in 26 synoptic stations located in the western half of the country were used. After reviewing and controlling the quality of station statistics and eliminating statistical deficiencies, the homogeneity of the data was evaluated using the Run Test and the randomness of the data was accepted at a 95% confidence level. The Pearson and Spearman correlation coefficients as well as a multivariate linear regression method were used in communicate the frequency of days associated with dust storm with extreme and average temperature variables. In this study, the observational values of the frequency of days with dust storm were considered as dependent variables and the average temperature data and cardinal temperature variables were considered as independent variables. In order to analyze the correlation, the zoning map of the coefficients was prepared by IDW method in ArcGIS software.

The results showed that the highest correlation coefficient with FDSD index was related to the maximum temperature variable in Abadan station with 0.875 and the minimum temperature in Ahvaz station with 0.893. Also, with increasing FDSD index, correlation coefficient values increasedat Abadan and Ahvaz stations, the stations which had the highest number of dust days with 401 and 321 days, respectively, during the 25-year period. Multivariate regression modeling between FDSD and different temperature parameters in the western half of the country showed that the most important factors influencing dust events are the extreme temperature variables.In all 26 stations studied, there is a positive correlation between the minimum temperature and the frequency of days with dust storm, but this correlation is more significant in some stations and in Dehloran, Ilam, Kermanshah, Safi Abad and Sanandaj stations at 95% confidence level and also in Hamedan (Airport), Islamabad Gharb, Abadan, Ahvaz, Bandar Mahshahr and Bostan stations were significant at 99% confidence level. Meanwhile, the highest Spearman correlation coefficient between different temperature parameters and Frequency of Dust Stormy Days (FDSD) is related to the maximum temperature variable in Ahvaz, Abadan, Bostan and Bandar Mahshahr stations with correlation coefficients of 0.59, 0.57, 0.53 and 0.51, respectively, have been registered. The highest Pearson correlation coefficient between temperature parameters and frequency of days with dust storm is related to the minimum and maximum temperature variables, which were recorded in Ahvaz and Abadan stations with a correlation coefficient of 0.893 and 0.875, respectively. Regression models show that, in the best case scenario, the temperature variables of 81.2% (Abadan) and 79.3% (Bandar Mahshahr) determine the changes in the FDSD index.

The cardinal temperature variables are known as an important and influential factor in the formation of dust storms because increasing the values of Cardinal temperature parameters leads to excessive evaporation from the soil surface, which can provide a source of particles for the occurrence of such dust storms. It should be noted that the average temperature variable can also have an important effect on increasing dust events, but compared to the cardinal temperature variables, its effect is much less. The results of this study can be useful in managing the issues caused by dust storms and in the combating plans to desertification in the study regions. Also, the results of this research can be a new guide for predicting and modeling the phenomenon of dust storms in the country.

Water scarcity and subsequent social and economic crises have doubled the need to use new interdisciplinary knowledge-based approaches in reforming water resources management structures and policies and implementing water resources plans and projects. In the meantime, recognizing the interrelationships of the social sciences and their water resources systems has become a problem that its solution help us to correct existing dysfunctional structures. Therefore, the main purpose of this study is to prove the interrelationship of some social parameters and water resources on a continental scale with the use of software modeling tools. In this study, per capita data on renewable water resources and social parameters including the ratio of rural population to urban population, population density, number of Internet users and education index on an annual scale are considered. The statistical period of the data was 13 years (2005-2007) and this study was performed for 42 countries in Asia whose per capita water resources were declining. Then, using soft copmuting methods such as Artificial Neural Network (ANN), Decision Tree (M5) and Adaptive Fuzzy-neural Inference System (ANFIS), the interrelationship between per capita water resources and social parameters was modeled. Modeling results were evaluated by the criteria of determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) values. Finally, the results indicate the superior performance of ANFIS method compared to the other two models in evaluating the interaction of per capita water resources and social parameters. Also, after the ANFIS model, the M5 and ANN models had better performance, respectively.

The development of water waves through submerged and non-submerged vegetation is accompanied by a loss of energy through the resistive force of the vegetation, resulting in a decrease in wave height. Wave damping by vegetation is a function of cover characteristics such as geometry and structure, immersion ratio, density, hardness, and spatial arrangement, as well as wave conditions such as input wave height, duration, and wave direction. In the present study, the effect of geometric arrangement of vegetation with variable height on wave damping has been investigated using the Flow 3D numerical model. For this purpose, a channel with a length of 480 cm and a width of 10.8 cm, which has been previously used by Cox and Wu (2015) to study the effect of plant density with variable height on wave damping, is modeled. The operation of the three arrangements, including long to short arrangement, short to long arrangement, and zigzag arrangement, is examined under four different waves, all of which are linear waves. It should be noted that in this study, wave height is considered as an damping index. The results obtained by measuring the height of the waves at four different points along the channel show that the behavior of the waves in dealing with different arrangements follows a fixed pattern and also changes in the geometry of the vegetation can greatly lead to Increase the damping of the waves. The results show that a change in height arrangement can cause a change in damping of up to 7.1%.

In this research, using direct measurement method, the flow pressure in the composite channel with vegetation has been measured. The vegetation used in this study was made of PVC. Five different diameters with values ​​of 20, 25, 30, 40 and 50 mm, two longitudinal distances of 12 and 15 cm and two transverse distances of 6 and 8 cm according to the regular and irregular arrangement and the location of vegetation as the proposed variables were used in this study. For direct measurement of force, a special type of hydraulic flume called "knife edge hydraulic flume" was used. Based on the results of the experiment, it was found that with increasing vegetation diameter, the flow force increases, which is due to increasing the volume and reduction of the cross-sectional area of the vegetation. In addition, due to the effect of longitudinal and transverse distances of vegetation, it can be said by increasing these distances, the flow force rate showed a decreasing trend. This is due to the reduction of vegetation volume and the secondary flows caused by vegetation cover.

Due to the growing development of meta-models and their combination with optimization algorithms for modeling and predicting meteorological variables, in this research four metaheuristic optimization algorithms of Particle Swarm Optimization (PSO), Genetics Algorithms (GA), Ant Colony Optimization for Continuous Domains (ACOR) and Differential Evolutionary (DE) were combined with the adaptive neural-fuzzy inference system (ANFIS) model. The performance of four combined models developed with ANFIS model to predict the Frequency variables of Dust Stormy Days (FDSD) on a seasonal scale in Khuzestan province in the southwest of Iran was evaluated. For this purpose, hourly dust data and codes of the Word Meteorological Organization were used on a seasonal scale with a statistical period of 40 years (1980-2019) in seven synoptic stations of Khuzestan province. The results of good fit indices in the training and testing phase showed that there is no significant difference between the ANFIS method and other combined models used. R and RMSE values of the best combined model (ANFIS-PSO) from 0.88 to 0.97 and 0.10 to 0.19, respectively, and in the ANFIS model from 0.83 to 0.94 and 0.11 to 21, respectively, were variable. The results also showed that the combination of optimization algorithms used with the ANFIS model does not significantly improve the results of the model compared to the individual ANFIS model.

Labyrinth weirs are among the hydraulic structures that are constructed to regulate the water level and control the flow in canals, rivers and reservoirs of dams. This structure is designed to transmit large currents in low heads by increasing the effective length of the overflow crown. The crest axis of this type of weirs is indirect and in showing the horizontal surface, the overflow is composed of interconnected walls. Labyrinth weirs are repeated with triangular, trapezoidal, rectangular and arc geometries alternating in flow width. The main criterion in the labyrinth design of the weirs was to increase the flow transmission capacity on the overflow with a fixed canopy and for a certain height of the water level upstream of the weir. In this study, 145 laboratory data were used, including four types of arc labyrinth weirs with different arc radius and tangent lengths. The discharge efficiency was introduced as a dimensionless parameter  using dimensional analysis. The relationships between  and the critical depth of flow (y_C/P) were obtained using graphs for two different types, R2 = 0.983 and R2 = 0.998. Then a graph to y_(C_Lt )/P and H_t/P a relation to calculate the critical depth were presented. The results showed that the calculated flow rate obtained using yc is consistent with laboratory values so that the relationship between them is R2 = 0.982.Labyrinth weirs are among the hydraulic structures that are constructed to regulate the water level and control the flow in canals, rivers and reservoirs of dams. This structure is designed to transmit large currents in low heads by increasing the effective length of the overflow crown. The crest axis of this type of weirs is indirect and in showing the horizontal surface, the overflow is composed of interconnected walls. Labyrinth weirs are repeated with triangular, trapezoidal, rectangular and arc geometries alternating in flow width. The main criterion in the labyrinth design of the weirs was to increase the flow transmission capacity on the overflow with a fixed canopy and for a certain height of the water level upstream of the weir. In this study, 145 laboratory data were used, including four types of arc labyrinth weirs with different arc radius and tangent lengths. The discharge efficiency was introduced as a dimensionless parameter  using dimensional analysis. The relationships between  and the critical depth of flow (y_C/P) were obtained using graphs for two different types, R2 = 0.983 and R2 = 0.998. Then a graph to y_(C_Lt )/P and H_t/P a relation to calculate the critical depth were presented. The results showed that the calculated flow rate obtained using yc is consistent with laboratory values so that the relationship between them is R2 = 0.982.Labyrinth weirs are among the hydraulic structures that are constructed to regulate the water level and control the flow in canals, rivers and reservoirs of dams. This structure is designed to transmit large currents in low heads by increasing the effective length of the overflow crown. The crest axis of this type of weirs is indirect and in showing the horizontal surface, the overflow is composed of interconnected walls. Labyrinth weirs are repeated with triangular, trapezoidal, rectangular and arc geometries alternating in flow width. The main criterion in the labyrinth design of the weirs was to increase the flow transmission capacity on the overflow with a fixed canopy and for a certain height of the water level upstream of the weir. In this study, 145 laboratory data were used, including four types of arc labyrinth weirs with different arc radius and tangent lengths. The discharge efficiency was introduced as a dimensionless parameter  using dimensional analysis. The relationships between  and the critical depth of flow (y_C/P) were obtained using graphs for two different types, R2 = 0.983 and R2 = 0.998. Then a graph to y_(C_Lt )/P and H_t/P a relation to calculate the critical depth were presented. The results showed that the calculated flow rate obtained using yc is consistent with laboratory values so that the relationship between them is R2 = 0.982.Labyrinth weirs are among the hydraulic structures that are constructed to regulate the water level and control the flow in canals, rivers and reservoirs of dams. This structure is designed to transmit large currents in low heads by increasing the effective length of the overflow crown. The crest axis of this type of weirs is indirect and in showing the horizontal surface, the overflow is composed of interconnected walls. Labyrinth weirs are repeated with triangular, trapezoidal, rectangular and arc geometries alternating in flow width. The main criterion in the labyrinth design of the weirs was to increase the flow transmission capacity on the overflow with a fixed canopy and for a certain height of the water level upstream of the weir. In this study, 145 laboratory data were used, including four types of arc labyrinth weirs with different arc radius and tangent lengths. The discharge efficiency was introduced as a dimensionless parameter  using dimensional analysis. The relationships between  and the critical depth of flow (y_C/P) were obtained using graphs for two different types, R2 = 0.983 and R2 = 0.998. Then a graph to y_(C_Lt )/P and H_t/P a relation to calculate the critical depth were presented. The results showed that the calculated flow rate obtained using yc is consistent with laboratory values so that the relationship between them is R2 = 0.982.

The increasing incidence of dust storms indicates the dominance of desert ecosystems in each region. Therefore, in order to properly control and manage dust storms, it is necessary to be aware of the temporal-spatial changes of this phenomenon and the need to predict and model it. In this study, in order to predict the variable frequency of days with dust storm (FDSD), the results of two hybrid methods under the titles of support vector-wavelet (W-SVM) and support vector-artificial plant algorithm (AF-) (SVM) was compared with the individual support vector machine (SVM) model. For this purpose, hourly dust data and codes of the World Meteorological Organization were used on a quarterly scale with a statistical period of 40 years (2018-1980) in five selected synoptic stations of Sistan and Baluchestan province. Explanation coefficient, root mean square error, mean absolute error value and task strain coefficient were used to evaluate and compare the models. The results of goodness-of-fit indices in the training and testing phase showed that the hybrid structures used provide acceptable results in modeling the FDSD index. Support-wavelet car vector hybrid model with correlation coefficient (R2 = 0.911-0.984), root mean square error (RMSE=0.314-0.397), mean absolute error value (MAE=0.236-0.335) And clutch saturation coefficient (NS = 0.927-0.965), had better performance than other models used in predicting the FDSD index. The results of this study can be effective in managing the consequences of dust storms and desertification programs in the study areas.

Sharp crested weirs are one of the most important structures in the river intake and are the most common instruments for measuring the intensity of flow in open channels, which are widely used in water transmission systems and irrigation and drainage canals to regulate water levels and floods. One of the types of sharp crested weirs is angular weir, in which by increasing the effective length of the crown, more discharge is allowed to pass with less head, resulting a higher efficiency and consequently a reduction in irrigation system costs. In this study, 165 existing laboratory data obtained from two flumes with widths of 0.5 and 0.52 m and with seven ratios of weir length to flume width (L/B) of 1.14 and 3.86 and six weir crown heights of 0.10 m to 0.506 m were used in free flow conditions. In this study, the critical depth of flow passage over the weir crown was used to calculate the flow rate. Also, a new function was developed to calculate the flow rate directly and without the need for flow coefficient by presenting the geometric coefficient of the weir, including all the geometric characteristics of the structure. The results of this study showed that by increasing the height of the crown, the flow head and the weir angle relative to the flow horizon increase. The results also showed that the new relationship with R2 = 0.9984 has high accuracy for measuring critical depth and flow rate.

In this research, a special form of nonlinear weirs called piano key overflows has been studied. In this type of weirs, unlike congressional weirs, the openings are sloping inwards and outwards one by one.so this study was conducted with the aim of laboratory study of the discharge coefficient of the arched piano key in the plan in free flow conditions. In this research, 27 models of arched piano key overflow structures with three arc lengths of 1.20, 1.40, 1.60 m, three crown lengths of 5.10, 4.30, 3.20 m and three crown heights of 0.30, Made 0.20, P = 0.15 m. All models were made of glass with a thickness of 10 mm.The models were compared in terms of height effect, key width ratio and arc effect under the same effective length conditions. The results showed that increasing the flow rate and consequently increasing the dimensionless ratio, leads to a decrease in watering coefficient and in fact has the opposite effect on the permeability coefficient of piano switch weir in free flow. Also, by increasing the dimensionless ratio, the flow currents from the output switch interfere with each other, and as a result, due to the local immersion, the overflow coefficient of the overflow of the arched piano switch in the free flow decreases. The arc model with an arc length of 1.40 m with 1.27 had the highest and the arc model with 1.20 m and 1.22 had the lowest permeability coefficient in free flow. Finally, it was found that the arc model has a higher permeability coefficient than the direct model, which indicates the positive effect of the arc. Also, in order to estimate the permeability coefficient of free flow and Q flow rate, relationships were provided for the overflow of the piano switch in the arc position.

The impact of the dust phenomenon in Iran is so great that it has involved more than half of the country's provinces in some way with the problems and limitations of this natural phenomenon, which in addition to environmental impacts, has disrupted the implementation of sustainable national development plans and so far it has had and will have many negative consequences. The increase in dust storms in recent years in the east and southeast of the country, especially in Sistan and Baluchestan province, and consequently the decrease in air quality in these areas, has doubled the importance of Forecasting this phenomenon. On the other hand, most domestic studies in this field are related to the process of small-scale dust phenomena, synoptic studies, and its satellites. Therefore, considering that this phenomenon has had adverse effects and negative consequences in the social, economic, and health fields of the people, it is necessary to study, forecast, and measure its relationship with climate variations.

This study aimed to compare the performance of SARIMA and Holt-Winters time series models with artificial intelligence methods including neural networks based on radial base functions (RBF) and adaptive neural-fuzzy inference system (ANFIS) to forecast the frequency of dust storm days (FDSD) in the next season. For this purpose, hourly dust data and codes of the World Meteorological Organization were used in five synoptic stations in Sistan and Baluchestan province with a statistical period of 25 years (1990-2014). The observations of meteorological phenomena are recorded once every three hours, a total of eight times a day. In these observations, the visual phenomena of climate are defined according to the guidelines of the World Meteorological Organization in 100 codes (00-99), in which 11 codes are used to record and report the phenomenon of dust in different meteorological stations. Following the time series of days with dust storms, the FDSD index was forecasted using four methods SARIMA, Holt-Winters, RBF, and ANFIS.

According to the results of the time series, the FDSD index in Saravan, Khash, Iranshahr, and Zahedan stations has relatively small variations that are scattered throughout the time series, but with the increase in the number of dust days in Zabol station, the scattering of the variations has decreased and its intensity has increased. Also, the peak values of dust are concentrated next to each other, which indicates the occurrence of successive dust storms in this station from 2000 onwards. As can be seen in the ACF and PACF diagrams of the studied stations, significant time intervals indicate the correlation between the time values that make it possible to modeling and forecasting future values (next season) of the FDSD index for all five stations studied. According to the functions of partial autocorrelation and autocorrelation, the range of change of attraction and the moving average was determined, and using the appropriate evaluation criteria, the best time series model was extracted for each station. In the Dickey-Fuller test, the significance level was considered to be P-Value < 0.05. According to the test, only the time series of Zabol stations is unstable, which confirms the results of the ACF and PACF diagrams of the studied stations. The results showed that the ANFIS method performed better than other methods in all study stations; So that in this method, the evaluation criteria of R, RMSE, MAE, and NS are varied from 0.72, 0.57, 0.42 and 0.71 to 0.95, 0.51, 0.40 and 0.96, respectively. Also, the average frequency of days with dust storm on a seasonal scale in the studied stations varied from 1.06 to 7.11, respectively, so that with increasing FDSD index in the stations, the forecasting accuracy of all methods increased. In the SARIMA time series model, the correlation coefficient (R) between the observed and forecasted values of the FDSD index was increased from 0.64 to 0.79. For Holt-Winters, RBF, and ANFIS methods, the R-value also varied from 0.70 to 0.87, 0.69 to 0.92, and 0.72 to 0.95, respectively. Also, based on the results of the observed and forecasted values, with the increase of FDSD index in the studied stations (progress from Saravan station to Zabol station), the relationship between observed and forecasted values in all methods (time series models and artificial intelligence methods) find more compatibility with the Semi-constructor of the first quarter. The results of the Z test also showed that the assumption of zero-based on the mean equal of the time series of observed and forecasted values of the FDSD index, in none of the studied stations based on ANFIS and RBF methods at 1% error level and based on SARIMA and Holt-Winters time series models are not rejected at the 5% error level.

The results showed that with the decrease in the frequency of days with dust storms in Saravan and Khash stations, the Holt-Winters time series model showed almost the same and higher performance than the RBF method, which indicated the high capability of this model to forecast low values FDSD index. The results also showed that the SARIMA time series model compared to other forecasting methods did not have a high ability in forecasting the FDSD index in any of the studied stations. Also, despite the low frequency of days with dust storms in Iranshahr station compared to Zahedan station, all FDSD index forecasting methods have better performance and more accurately than Zahedan station based on evaluation criteria, which can be searched due to the presence of a complete series without FDSD index termination at Iranshahr station. The results of this study can be useful in forecasting and managing the consequences of dust storms in the study areas. On the other hand, in forecasting the FDSD index in Sistan and Baluchestan province, the optimal predictor model has been complex. For all of the stations studied, the model that used three or four steps of the predictive delay was recognized as the best predictor model. Therefore, particles leftover from previous storms could be an important reason for the impact of the last few seasons’ storms on the formation of dust storms in future seasons.

This research was conducted to determine the need for green water space in District 4 of Tehran Municipality and to calculate the required water resources for the total green space of the region. To determine the water need of green space plants, the California method (wucols) was used and to calculate the evaporation and suspension of reference and the amount of more load, the climatic data of the North Tehran Meteorological Station in a period of 30 years (1986-2016) was used. The net area of green space in the study area was 5683 hectares, which includes 215.20 hectares of urban Park, 5311.73 forestry and 156.3 hectares of other green space divisions. Irrigation efficiency in this area was estimated at an average of 35 percent. The results showed that the amount of water required annually for irrigation of green space in the area was estimated at about 48.6 million cubic meters with an efficiency of 35 percent and about 47.3 million cubic meters with an efficiency of 45 percent. With the scenario of increasing irrigation efficiency by only 10%, 1.3 million cubic meters per year, only water resources were saved. According to the amount of water that can be extracted from regional water resources, which is equal to 23.62 million cubic meters per year, the annual water shortage of the region is calculated and its amount is equal estimated to 24.98 million cubic meters with an efficiency of 35% this requires careful management and immediate operational solutions.

Today, the occurrence of dust storms, in addition to having a direct impact on people's lives, has adversely affected the agricultural sector, including agronomy, horticulture, natural resources and the environment. This study was carried out to investigate the trend of frequency changes of days with dust storms and its relationship with Standardized Precipitation Index (SPI), using hourly and daily dust data and monthly rainfall data of 21 selected synoptic station in the west and southwest of the country with a seasonal scale over a 25 years (1990-2014) statistical period. After quality controlling all dust data for abnormality, using Kolmogorov-Smirnov test, and also measuring the long-term memory of time series using the Hurst coefficient, non-parametric Mann-Kendall and Spearman tests were used at a 95% confidence level to examine the trend of abnormal data. Then, the seasonal SPI drought index was calculated in the proposed stations and its trend was analyzed by parametric linear regression test method. Also, the zoning of the Mann-Kendall statistics and the linear regression of the variables were performed by IDW method in ArcGIS software. Then, to investigate the climatic relationship with dust storms, stations with SPI index and also variable frequency of days associated with dust storms had a significant trend (dust storms in both Mann-Kendall and Spearman methods and SPI index in the method of linear regression), were analyzed by Pearson correlation technique. The results showed that out of a total 21 series of seasonal data; the stations of Hamedan (Nozheh), Ilam, Islamabade-Gharb, Keramnshah, Sarpole-Zahab, Abadan, Bandare-Mahshahr, Bostan, Dezful, Masjedsoleyman, Safiabad, Dogonbadan, Sanandaj, and Khorramabad have a high correlation between standard precipitation climatic index and the frequency of dusty days. So that the highest correlation between the number of dusty days and SPI drought index was attributed to Dezful, Bostan and Masjedsoleyman stations with the Pearson coefficients of -0.920, -0.913 and -0.911 and P-Values of 0.002, 0.025 and 0.044, respectively. The results of this study could be useful in managing the consequences of dust storms in the study areas.

The increase in dust storms occurrence in recent years in southwestern Iran, especially in Khuzestan province, and consequently the decrease in air quality in these areas, has doubled the importance of forecasting and linking this phenomenon with climate variations. The aim of this study was to investigate the efficiency of hybrid Genetic-Annealing (GA-SA) and Genetic Algorithm (GA) methods for selecting optimal input variables in forecasting the frequency of days with dust storm (FDSD). For this purpose, hourly dust data and meteorological organization codes, as well as climatic data including maximum temperature, minimum temperature, average temperature, total rainfall and maximum wind speed on a seasonal scale with a statistical period of 35 years (1984-2018) in seven synoptic stations in Khuzestan province were used. Then, by having a time series of FDSD index and other climatic variables, it was compared to the efficiency of different modes of input variables, in order to forecasting the frequency of days with dust storm in the next season. The results showed that the hybrid Genetic-Annealing method (GA-SA) had the best performance among all the modes of selecting the input variables; In this case, the evaluation criteria of R, MAE and RMSE varied from 0.91, 0.29, and 0.44 to 0.99, 0.13 and 0.17 in the studied stations, respectively. Also, the average frequency of days with dust storm on a seasonal scale in the studied stations varied from 1.68 to 4.10, respectively, so that with increasing FDSD index in the study station, the predictive accuracy of all modes increased so that in the first forecast state (based solely on the FDSD index), the correlation coefficient between the observational values of the days associated with dust storms and its computational values increased from 0.87 to 0.95. For the second case (forecast based on frequency of days with dust storm and all Auxiliary Characteristics, ie FDSD & AC), the third mode (based on the optimization of the Genetic Algorithm) and the fourth mode (based on the hybrid Genetic-Annealing method) the correlation coefficient also varied from 0.93 to 0.94, 0.91 to 0.97 and 0.94 to 0.99 in the studied stations, respectively. In general, by comparing the modes used, the hybrid Genetic-Annealing method (GA-SA) performed the best, followed by the Genetic Algorithm (GA). The results of this study can be useful in managing the consequences of dust storms and desertification programs in the study areas.