mohammad javad zeynali

In this research, the objective is to study the process of contaminant transport and pumping in a laboratory model. A two-dimensional laboratory model was used in this research. The length, width, and height of the model respectively were 140 cm, 5 cm, and 57 cm. Chambers were provided on the left and right sides of the model to store water and create a constant hydraulic head. The material used in this model was glass beads, which act similarly to sand particles. Sampling tubes were installed at five points A to E along the length of the model and at three depths. Finally, at specific times after the contaminant release, a sample of the contaminant was injected and its concentration was analyzed. The research results showed that at a gradient of 0.05, the contaminant transport rate was significantly higher than at a gradient of 0.014. Therefore, the depth penetration of the contaminant is less at a gradient of 0.05 than at a gradient of 0.14. On the other hand, contaminant pumping at points C2 and D2 showed that pumping from point C2 more effectively reduced the contaminant concentration in the entire laboratory model. This holds for contaminants with other concentrations as well. Contaminant concentration and hydraulic gradient are two important factors in the amount of contaminant transport. Also, for effective contaminant pumping, the best location for pumping contaminated water is somewhere near the seepage site and in the path of the contaminant movement.

The efficiency of groundwater remediation by the pump-and-treat (PAT) method is affected by several components. The most important of these components is the pumping wells' location. In this research, hybrid optimization-simulation models were developed to find the appropriate groundwater remediation strategy using the PAT method. The GA-FEM and NSGA-II-FEM models were used to solve two optimization problems for a real aquifer (Ghaen aquifer). These optimization problems were investigated from one objective problem and two objective problems in three scenarios. In solving the single-objective optimization problems, the objective was to determine the optimal location of three, five, and seven pumping wells with a rate of 600 m3/day to minimize the mean of carcinogenic human health risk. The results indicated that the GA-FEM model has a good efficiency with 356.2302×10-6, 356.2253×10-6, and 356.2226×10-6 for three scenarios, respectively. The results indicated that increasing the number of pumping wells between scenarios one and two, 0.0013% and scenarios one and three 0.0021% improves the amount of mean carcinogenic human health risk. In the two-objective problems, the second objective function was defined as minimizing the drawdown of the groundwater head. The results of the two-objective problems in three scenarios indicated that the NSGA-II algorithm had a good performance and the NSGA-II algorithm provided a well-distributed set of solutions along the Pareto-optimal front. Also, the results indicated that when there are 5 pumping wells, the minimum mean of carcinogenic human health risk is 3.56226 and by adding two more pumping wells, this amount reaches 3.56225, while the rate of groundwater drawdown increases by 20 meters. Therefore, increasing the number of pumping wells from one limit not only does not have a significant effect on reducing pollution but also causes an increased groundwater drawdown.

The goals of this research include investigating the efficiency of the finite element method and its combination with meta-heuristic algorithms to solve the optimization problem of the pump and treat (PAT) system. In this research, the hybrid optimization-simulation models were developed to determine the optimal groundwater remediation strategy using the pump and treat (PAT) system. The results indicated that when we consider minimizing the contaminant in groundwater at the end of the remediation period as the objective function, locating the pumping wells in the path of the contaminant flow and close to the contaminant source. In a single objective problem, the GA-FEM model with an average value of 0.0005036 in five runs of the model had the best performance among other models. The results of the two-objective problem indicated that MOMVO-FEM, despite a few solutions in optimal Pareto-front, could find a better location for pumping wells. Finally, it can be said that among factors such as the location of pumping wells and pumping rate, the most influential factor in choosing the right pumping and treatment policy is the proper location of pumping wells. Also, the location of contamination pumping wells does not necessarily correspond to the location of the contamination seepage.

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 study, seven models such as multivariate regression, Contemporaneous Autoregressive-Moving Average (CARMA), CARMA-ARCH (Autoregressive Conditional Heteroskedasticity), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Genetic Programming (GP) were investigated to downscaling the maximum daily temperature of Birjand synoptic station using 26 predictor’s parameters that resulting from the fifth Intergovernmental Panel on Climate Change (IPCC) report and compared. The max daily temperature values measured from 12/03/1961 until 20/12/2005. In all mentioned methods from 26 predictive parameters using the Pearson correlation test, 15 parameters were selected that have a high correlation with the max daily temperature values. The results of evaluating the accuracy and model indicated that from the same models such as GP, ANFIS and SVM, the GP model has the least amount of errors (about 4 °C) and in the regression models (multivariate regression and SVR), SVR have been lowest error rate (about 1 °C) and the highest accuracy in simulated max daily temperature values. The results of the investigation the error rate of the mentioned models indicated that after the SVR model, two CARMA and CARMA-ARCH stochastic models have high and acceptable accuracy about 97 percentages. In general, the results of the simulation the max daily temperature indicates the best accuracy of regression toward another methods. One of the reasons for the results of the SVR model is to optimize the parameters of the model using the ant colony algorithm for estimating the maximum temperature values of the Birjand synoptic station.

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.

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.

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.