محمد جواد زینلی

Persian language has long had a great impact on Ottoman language and literature and was once the main language of this region and many Ottoman thinkers have researched and followed important Persian texts and have written many commentaries on important texts of Persian literature; Among them, we can mention the commentary on Soodi on Hafiz's Divan and the commentary on Anqravi on Rumi's Masnavi. Since the description of the text is also a kind of critique of the text and the common description in the past and among Muslims, it focuses more on word and content, the aim of this article is to describe the Turkish commentators and their view of prominent texts such as Hafiz's Divan. Masnavi and seeks to answer the question of what cultural, social and political contexts have influenced the description of these thinkers and what are the signs of social, historical... critique in the method of library study, filing method and data analysis. Can their details be seen? It seems that although most of these explanations are word descriptions, they are different from the view of traditional commentators, and we see traces of attention to the history and society of the poet's time in these explanations. Paying attention to the typography and examining the correctness of the attribution of a verse or poem to the poets in question, mystical interpretations, paying attention to the life of the poet and individuals in the description of the text, are important features of these explanations.

The geographical map of the countries shows that the political boundaries are not aligned with the water basins. The existence of transboundary rivers has led to divergences over the water availability of these rivers in many countries. Competition and conflict have been created for gaining more profit. What makes the competition over the border waters more serious is the nature of the international relations of the upstream and downstream Countries that transform water use into political leverage. In Iran, the limitation of fresh water resources and the need to plan for the proper utilization of diverse water resources capacities, highlight the importance of obtaining the right of transboundary rivers. This part of the water is effective in preventing pressure on internal resources, some natural damage caused by drought and water scarcity, and some internal conflicts between basins. Considering the research done on transboundary rivers, which have often been politically or geographically examined, So far, no research has been conducted on the development and prioritization of strategic policies for the extraction of the water rights of Iranian transboundary rivers. In this study, the strategic policies that the government can adopt to obtain the water-right of the Iranian transboundary rivers were determined based on the expertise of experts in the fields of water sciences, environment, geomorphology, political sciences and middle managers of the water industry. To this aim, a questionnaire was prepared in with 5 Criterion and 12 strategies. Then, its validity was evaluated by the relevant experts. After conversion of qualitative values to 20 quantitative questionnaires, the reliability of the questionnaire was confirmed by Cronbach's alpha in SPSS statistical software. This coefficient was calculated 0.735. The AHP method was then prioritized by Export Choice software. The ANP method was prioritized in the MATLAB programming environment. Then, the results of both methods were compared. In the sensitivity analysis by the Three Antifilo method, the numerical value of the most sensitive criterion was calculated and then the weight of the other criteria was modified accordingly. The threshold percentage and change in weight of the criteria were determined. Afterward, the sensitivity coefficient of the criteria was calculated and the numerical value of the most sensitive criterion was modified. The other criterion was modified based on the modified critical criterion. To analyze the sensitivity of strategic policies the fallowing steps were taken: First, numerical values of threshold weight and its percentage and critical degree were calculated; Second, sensitivity coefficients of strategic policies, which are the inverse of the most critical strategies, were calculated and compared; Third, The numerical value of the most sensitive strategy was corrected; and at the last step of this method, the values of each strategic policy were normalized to the most sensitive criterion. The results showed that the two criteria of benefit and timing with weight of 26.26 and 0.13 were in the first and last priority, respectively. The final prioritization was similar in both ways, except in the second, fourth and ninth strategy of the initial table. The strategy of controlling more outflows from the country and applying pressure to reduce tariffs for importing high-quality virtual water into the country, in the AHP method with 11.88% and in the ANP method with 11.08%, placed as first priority. In addition, A11 strategy was the last priority with 8.17% by AHP and 6.11% by ANP. Also, profitability with the coefficient of 0.185 was introduced as the most sensitive criterion. Based on it, the weight of the other criteria was modified. Therefore the prioritization of the criteria remained unchanged in comparison with the results of the sensitivity analysis of strategies based on each criterion separately. It showed that the most sensitive strategic policy is the strategy of cooperating with neighboring countries for better access to seawater, changing the pattern of cultivation with low water requirement and thriving in other economic potentials of the neighboring country to reduce the tendency for highly water-intensive activities. Its regarding coefficient was 0.178 which is the highest value, based on the profitability criterion. In the final step after normalizing the weights of the strategies according to the critical criterion, the prioritization of the strategies with little change in weights also remained unchanged. Advancing the first policy will be effective in short term, but will not entail economic costs, and it may create political strain on the diplomatic relations of the two countries. Advancing the second priority will be more time consuming, but not, economically and politically costly. Like the first priority, the second can improve Iran's political standing in the region, while it can cost diplomatic means on the country. The last priorities are the strategic policies that are economically costly for the country and have a negative impact on the other two countries' relations.

in this research, two conflicting objective functions used to solve the problem of optimization operation of Sistan's Chah Nimeh reservoirs. The first objective function defined minimizing the total of second power of difference between agricultural demand and release and the second objective function defined maximizing the reliability index. In this study, to compare the studied algorithms, the criteria of the algorithm's run time, the number of solutions in the optimal Pareto front, and distance, dispersion, convergence and generation distance were taken. The results of the study of Meta-Heuristic algorithms indicated that among MOPSO, MOGOA and MOALO algorithms, MOALO and MOGOA algorithms were more efficient than MOPSO algorithm. According to the performance criteria of the algorithm's run time and the dispersion criteria, the MOPSO algorithm showed high efficiency and according to the performance criteria of the distance, convergence and generation distance criteria, the MOGOA showed high efficiency. According to the performance criteria of the number of solutions on the optimal Pareto front MOALO algorithm showed high efficiency. Also, MOALO and MOGOA algorithms effectively covered optimal pareto front. It can be said, the solutions of these algorithms find in themselves optimal pareto front, create a rich set of optimal solutions that not only effectively cover the optimal Pareto front, but also dominate the solutions of the other two algorithms. Therefore, it seems that none of these performance criteria can alone determine the superiority of an algorithm than other algorithms in solving an optimization problem.

In this paper, an optimization simulation hybrid model is used to evaluate the optimum pumping policy in an artificial aquifer. In this study, the groundwater level drop was investigated in a hypothetical free aquifer with a surface area of 1.5 square kilometers and three different hydraulic conductivity with a thickness of 100 meters, as well as ten pumping wells. In order to estimate the groundwater level of the aquifer, the finite element method was used and to optimize the position of the wells, used the algorithm of the ant colony and finally the FEM-ACO model was presented. The position of wells with a specific discharge is optimized to minimize groundwater level losses in the aquifer. In this regard, the number of different number of ants, the effect of Foramen evaporation and the effect of the elite percentage of an ant collection on the value of the objective function were investigated. The results showed that sequencer antler algorithms and elite ant anchor algorithms with almost identical performance have the best performance among the antler algorithms, and after that, the antler community algorithm and the max-minimal ant antler algorithm are in the next ranks. All of the ant colony algorithms arrived too quickly into convergence, which makes this early convergence a suitable global optimum due to the use of chain constraints. Finally, after considering the proposed model, the proper position of the wells was determined. The results also showed that the maximum water loss in the aquifer is about 2.5 m.

Numerous studies in optimization of water supply networks have been allocated to cost reduction of these infrastructures. In general optimization methods, minimizing the costs is the unique purpose, but applying this view point for the case of water supply systems may lead to reduction of the utility and efficiency of the system. Therefore, some multi-objective optimization models have been used to decrease the large amount of costs in these systems and to increase their operation efficiency. The present study has attempted to illustrate a case study for a town of Kerman in order to decrease the costs of rebuilding pipe network using the Imperialistic Competition Optimization Algorithm. So, the network simulation was performed with Water GEMS software. Firstly, the water supply network of the study area was simulated in the Water GEMS model. Next, the properties of water supply network were inserted. Then, using the standard pressure and speed constraints, the optimal options of the algorithm were obtained. By re-running the results of this optimized model in the Water GEMS model and the limits’ checkup, the total costs were estimated and compared. Analysis of the optimal model results showed that in comparison with the state prior to the network optimization, the Imperialistic Competition Algorithm has significantly reduced the cost function of the network.

Rainfall-runoff modeling is an essential process and very complicated phenomena that is necessary for proper reservoir system operation and successful water resources planning and management. There are different methods like conceptual and numerical methods for modeling of this process. Theoretically, a system modeling required explicit mathematical relationships between inputs and outputs variables. Developing such explicit model is very difficult because of unknown relationship between variables and substantial uncertainty of variables. So far performance models such as neural networks, multivariate models with auto moving average is studied for modeling the rainfall-runoff. So, in this study CARMA and ANN models studied in rainfall-runoff modeling.
In this research, the multivariate contemporaneous autoregressive moving average (CARMA) models and artificial neural networks (ANN) were evaluated to rainfall-runoff modeling. we define 3 scenario for ANN model. In order to use CARMA and ANN models total annual precipitation and runoff time series in the period of 1975-2015 as for Nazloochaei the catchment area, in 44° 49 ' in latitude and 37° 40 ' longitude in the province of West Azerbaijan was used. At first, we checked the data in terms of randomness, trend and Homogeneity by run test, Mann-Kendall test and Wilcoxon test. And then we separated data in two group. One group including 80 presents of data for training and 20 percent of data for validation was assigned. Performance criteria that used, was root mean square error, Nash-Sutcliffe and correlation coefficient. Performance criteria that used, was root mean square error, Nash-Sutcliffe and correlation coefficient.
The results of this research indicated that the CARMA model had the efficiency accuracy more than ANN model, because root mean square error in CARMA model was equal 7.7 and that was in ANN model 9.50 m3/s. Also, CARMA model according to the Nash-Sutcliffe criteria and R2 equal to 0.41 and 0.54 had better performance than the ANN model. However, the value of these performance criteria in ANN model was equal 0.45 and 0.80. So CARMA model has more Accuracy than ANN model in rainfall-runoff modeling.
According to the obtained results, using multivariate ARMA models caused to decrease in model error up to 18 percentages. So CARMA model had suitable performance in compare with ANN model, and this subject show the importance of to consider Random component in modeling. So CARMA model had suitable performance in compare with ANN model, and this subject show the importance of to consider Random component in modeling.

Optimizing parameters of nonlinear Muskingum model done by the method of try and error and numerical. These methods are difficult and time-consuming. But Meta-Heuristic Algorithms can indicate the good estimation of these parameters with high-speed and more precision. In this study investigated performance of continuous ant colony algorithm (ACOr) for estimation of optimal parameters of the nonlinear Muskingum routing model and we used sum of squared error criteria to evaluate that. The results indicated that continuous ant colony algorithm has good efficiency on Wilson flood and Wye River flood with SSE=36.7679 and 37944.14. After ensuring the efficiency of ACOr algorithm, we used nonlinear Muskingum routing model investigated for Karoon River and the value of SSE was equal to 144691.735 that indicated higher performance in comparing to bee colony algorithm (SEE = 177161.4). Also, in this study the three flood routing have investigated by the convex method. This method didn’t has good performance on Wilson flood and Wye River flood but in Karoon River flood routing indicated better performance than nonlinear Muskingum methods. This model provided good estimation of peak of flow discharge, that this issue is very important for Implementation of flood warning systems.

One of the most important issues in the field of optimizing water resources management is the optimal utilization of the dam reservoirs. In the recent decades, the optimal operation of dams has been one of the most interesting issues considered by water resources planners in the country. Due to the complexities of the typical optimization methods, employing an evolutionary algorithm is regarded here. One of the most significant algorithms is the ant colony algorithm. So the aim of this study is to optimize the delivery of Golestan and Voshmgir reservoirs to meet the needs of the down lands using the elite ant colony algorithm, maximum – minimum ants, ranked ants, and particle swarm algorithms, and to compare the performance of these algorithms with each other. The considered decision variable was the release of the reservoirs in the above- mentioned dams. In this study, the data over a 5-year period, from 2006-2007 to 2011-2012, was used for modeling. The results showed that all algorithms could optimize the release amount optimally; however, the elite ant algorithm with the objective function value of 0.6407 estimated the release values with great accuracy in both dams. Also, the particle swarm algorithm with 1.275 of the objective function value was well-matched with the release values. The ranked ant algorithm with 18.924 and Max-Min ant with 26.431 of the objective function valuewere, respectively, at the next levels of performance optimization of the release values from Golestan and Voshgar dams.

Prediction of climate change using the recorded data from reference periods requires precise methods that are able to accurately detect fluctuations and predict changes for future periods. In this study, six multivariate regression models, ANN, SVR, ANFIS, SVM and GP, were investigated and compared for down scaling the daily mean temperature of the Urmia synoptic station, using 26 prediction parameters taken from the fifth IPCC report. The average daily temperature values measured from 12/03/1961 to 20/12/2005 were used for the analysis. 16 of the 26 parameters having a high correlation with the average daily temperature values were selected for all methods using the Pearson correlation test. To investigate the modeling errors, the coefficient of determination, Root Mean Square Error, and effectiveness criteria were used. The results of the evaluation of the accuracy and modeling error showed that among the smart models, GP, ANN, ANFIS and SVM, the Genetic Programming model has the least amount of errors, and in the regression model (multivariate regression and support vector regression) support vector regression has the lowest error rate and the highest accuracy of simulated daily temperature values of the Urmia station. In general, the results of the simulation of the mean daily temperature indicate that regression has better accuracy than smart methods. Since in this study, we only used the data from the Urmia synoptic station, so the results are only valid for this station, and it is not safe to generalize the results for all stations.

the performance criteria have used in this study is including mean square error (MSE), sum square error (SSE), Nash_Sutcliffe and correlation coefficient. The result indicated the best ANNa model is ANNa2 with MSE equal 0.0017. Inputs in this model are Total Cation, Total Hardness and Calcium. The best ANNb model is ANNb3 with MSE equal 0.0012. Inputs in this model are Sodium adsorption ratio, pH, Total Hardness and Calcium. Also, the results indicated that using ACO algorithm for finding the best input parameters had increased neural network performance, in ANNb models than ANNa for validation network and for test network we see with increases inputs the performance of network increases. According to results we can say that against try and error for finding the best inputs, we can use the parameter that those had a high correlation between target parameter as first step. But parameters that have high correlation between target parameter, necessarily dont the best inputs. But the parameter that those had a high correlation between target parameter as inputs of neural network. Also, we find that the ACO algorithm can be used as a method of input variable selection and that improved the performance of neural network.

Accurate prediction of river flow is one of the most important factors in surface water recourses management especially during floods and drought periods. In fact deriving a proper method for flow forecasting is an important challenge in water resources management and engineering. Although, during recent decades, some black box models based on artificial neural networks (ANN), have been developed to overcome this problem and the accuracy privilege to common statistical methods (such as auto regression and moving average time series method) have been shown. In these research only attended change number of hidden layer and number of neurons for to approach to the best structure in neural network, and complex in proper network designand can’t be simply used by other investigators. In this study examined 15 the neural network learning function and the results indicated in the structure of the network with one hidden layer (ANN1),learnlv1, learnh and learnis by MSE=0.000158, 0.000185 and 0.000188, have been better performance than the other learning functions. And in the structure of the network with two hidden layer (ANN2),learnh, learnsomb and learncon learning function by MSE=0.000154, 0.000173 and 0.000176 have been better performance than the other learning functions.But on the other hand by ten times run this two models, learnsom and learngdm learning functions in ANN1 model and learnh and learnos in ANN2 model had most frequency among the best learning functions and thus it is better that the number of hidden layer not more than one, when we use back propagation network (that its learning function is learngdm). Because in this way we have more chance to achieve ideal response. But if we are going to increase network performance byincreasing the number of hidden layer, it is better that use the default of network and learngdm carefully.

The high cost problem of urban water supply systems, along with the complexity of the design and unsuitable operation problems cause that optimization of the system before applying any changes, has become the basic needs of managers in this area. Due to the complexity of nonlinear and unique design of these networks, in recent years engineers using artificial intelligence and search algorithms to solve this problem. In the present study find solutions for the network in a town of Kerman with help of fast messy Genetic and firefly Algorithms and network simulation software intended WaterGEMS. First the water supply network in the study area simulated in WaterGEMS model and the properties required for optimization algorithms have been extract, then using the standard pressure and speed constraints, these algorithms create optimal choices. By entering these results in WaterGEMS model and re-running for limits check, the cost estimates are discussed and compared. The results show that the optimization of, fast messy Genetic algorithm with 37.7% is able to reduce the cost function of network compare to pre-optimized network. Also Firefly algorithm in the amount of 34.4%, is able to reduce costs. Finally, we can say that both optimization algorithms used in this study have been able to achieve a dramatic reduction in project costs.

Estimation of sediment concentration in rivers is very important for water resource projects planning and managements. Various models have been developed so far to identify the relation between discharge and sediment load. One of the most common methods for estimating sediment of rivers is sediment rating curve. For better estimation of the amount of sediment based on the sediment curve rating equation¡ it is possible to optimize its coefficients. One of the methods used for optimizing the coefficients of the sediment curve rating equation is taking advantage of meta-heuristic algorithms. Nowadays¡ optimization algorithms are used regularly for solving complex and non-linear problem. The main objective of this research is the use of genetic algorithms and particle swarm to optimize the relationship between discharge and sediment discharge in Kohak station on the Sistan River and comparison the results of these models with sediment rating curve. For the calculation of sediment discharge by the models initially necessary statistics and information including flow discharge and sediment concentrations have been measured since 1981-2011 in the stations are collected. Genetic Algorithm(GA) and Particle Swarm Algorithm(PSO) models were coded in MATLAB. After the models were trained with 70% to 30% of the data at both stations were tested. Evaluation parameters efficiency such as coefficient of determination (R2)¡ root mean square error (RMSE) and Nash-Sutcliffe coefficients (CE) are used in evaluating the models. The results showed that the genetic algorithm with 33484.47 values at Kohak station has lowest root mean square error in all models. After genetic algorithm¡ Particle swarm algorithm with 34754.31 values has lowest values of the objective function.

One of the most important problems in water resources management is the optimal operation of dam reservoirs. Various methods have been applied to deal with optimization problems. Noting the inability of the conventional optimization methods to solve complex optimization problems, the Meta-Heuristic algorithms have been noticed more than ever. In this study, particle swarm, genetic and continuous ant colony system algorithms were used to optimize the operation of the Doroodzan reservoir. Furthermore, the effect of chain constraints inclusion on the framework of these algorithms were analyzed. The results indicated that regarding the chain constraints, all programs resulted in feasible solutions, where without chain constraints, in some cases the algorithm was unable to find a feasible solution. Therefore, applying these constraints in the framework of the algorithms improved their performances further. In finding the optimized solution, the Genetic algorithm had a better performance than the two other algorithms. Reliability criterion was used to evaluate the algorithms performances. Based on this criterion, as one of the most important criteria in determining system performance, the Genetic algorithm with the Reliability value of 0.964 had the most suitable performance.

As a crucial issue in aqua sciences, optimizing dam reservoirs exploitation has been studied with a variety of optimization techniques. One of these methods is using Meta-Heuristic algorithms such as Firefly and Ants Algorithms. Using the firefly algorithm, this study studies the exploitation optimization of Doroudzan reservoir in a 99-month period. The most sensitive parameter in sensitivity analysis of Firefly Algorithm, α, is known as the mutation rate. Selecting its appropriate value by Firefly worms leads to an appropriate solution and increases the efficiency of the Firefly Algorithm dramatically. To determine the efficiency of this algorithm in optimizing the utilization of the dam reservoir, the obtained results were compared with the results of Continuous Ant System and Ranking Ant System. The findings indicated that FACC algorithm with objective function rate of 4.196 had a satisfactory performance. ACOrCC and ACOrankCC algorithms with the values of 17.004 and 26.156 followed it respectively. In addition, FACC algorithm with a value of 0.959 had the highest reliability coefficient. The results indicated that regarding Chain constraints, all program performances led to feasible solutions; however, ignoring chain constraints, the Continuous Ant System algorithm was unable to find a feasible solution. Hence, applying these constraints in the main structure of this algorithm would enhance its efficiency significantly.