فهرست مطالب محمود محمدرضا پورطبری

Nitrate is one of the pollutants that affects groundwater in most aquifers of the country. In order to investigate the behavior of hydrogeochemical parameters of groundwater in the Ardabil plain aquifer, the results of qualitative data of 139 wells that were measured in both wet and dry seasons during the water year 2012-2011 were used. Using XLSTAT software, contamination was identified and samples were classified using principal factor analysis (PCA) and hierarchical clustering (HCA). Factor analysis method led to the extraction of five factors affecting groundwater quality with a total variance of 68.09 percent. Also, the results of factor analysis showed that the first, fourth and fifth factors are due to geogenic processes and the second and third factors are due to anthropogenic processes. The results of the hierarchical clustering method divided the samples into three groups, each group having two subsets. The set related to cluster one is spread in the northern, eastern, southern and parts of the center, west by mix(agriculture-garden), medium pasture and dry farming as well as urban areas. The set of the second cluster is often spread in the central, western and urban areas of the aquifer, with mix(agriculture-garden) and residential areas. The set of third cluster has been expanded to southwestern and urban areas. Generally, the existence of dissolution and ion exchange processes on the one hand, as well as leaching of fertilizers and incomplete development of the sewage system in the aquifer on the other hand has provided the groundwater pollution.

Urban water distribution networks consider as one of the essential infrastructural facilities and equipment in urban areas. The pipes are one of the primary and essential components of a water distribution network break during operation due to various factors. So, developing models for pipes failure rate prediction can be one of the most crucial tools for managers and stakeholders to the optimal operation of the water distribution network. In the last decade, various studies have performed to predict the failure rate of water distribution pipes using statistical and soft models - each of which has strengths and weaknesses. This study aims to present a new approach based on the development of a hybrid prediction model, considering the capabilities of soft and statistical models, to more accurately predict the water distribution network pipes failure rate compared to statistical and soft models used in previous research.

In order to achieve the study goals, 4-year (2015-2018) time duration statistics of Gorgan water distribution network characteristics including diameter, length, age, depth of installation, and the number of pipe failures used to predict future pipes failure rates. To modeling the pipe failure rate of the investigated network, five different models, including three statistical models (linear regression, generalized linear regression, support vector regression) and two soft models (feed-forward neural network, and radial basis function neural network) has studied. Optimal parameters of the models were selected based on appropriate statistical error indicators, including correlation coefficient (R), Mean Square Error (MSE), and Correlation Mean square error Ratio (CMR) for the training and testing data. In order to select the best model from different models to predict the failure rate of network pipes, the values of R and MSE indicators of the above models were calculated in the validation stage and compared with each other. Finally, to predict pipes failure rate more accurately, a new approach is developed based on the hybrid prediction model in which the predicted values of pipe failure rates by statistical and soft computing models considered as independent variables of the best model inputs and the observed values of failure rates as dependent variables of the best model outputs.

Comparing the values of R and MSE indicators of each statistical and soft computing model used in this study in the validation phase show that these models cannot predict the pipes' failure rate with reasonable accuracy. Feed forward neural network model with the highest R = 0.69 and the lowest MSE = 0.062 values has the best estimates. Using the new approach developed based on hybrid soft and statistical models, the R index is equal to 0.96, and the MSE index is equal to 0.046.

A significant increase in the R index (39%) and decrease in the MSE index (25%) through using the proposed hybrid approach compared to the feed-forward neural network model demonstrates that using the new approach provides perfect accuracy prediction of the pipes failure rate of the water distribution network.

Assessing the quality of available water is especially important to meet drinking needs. Therefore, it is important to prepare and formulate an approach that can provide a more appropriate evaluation with higher accuracy and taking into account the inaccuracies caused by instrumental errors and measurements. The aim of this study was to evaluate the groundwater quality of Zanjan plain based on a method similar to fuzzy ideal solution.

To achieve this goal,  57 and 59 groundwater quality samples in dry and wet seasons were used and the 28 quality parameters were analyzed in each sample. The relative closeness coefficient of each samples were determined based on fuzzy weight specified by the opinions of experts in this field for each quality parameters and the fuzzy decision matrix based on drinkable water quality standards. By calculating the relative closeness coefficient of each samples, the contamination ranking for each sample was determined.

The results show that the more polluted areas are mainly concentrated in the center and northwest of the plain and this is in complete harmony with the location of industrial centers (such as lead and zinc complexes) and the hydrogeological characteristics of the aquifer. In order to evaluate the accuracy of the ranking, the results of the proposed method were compared with the definite case of the method similar to the ideal solution. Examination of the qualitative parameters related to rank 1 (lowest pollution rate) shows that most of the parameters in this rank, which should represent the lowest pollution rate, have high values ​​in the definite method compared to the fuzzy method (for example, the amoun of the arsenic parameter located in rank 1 of the definite and fuzzy methods is equal to 0.4 and 0 micrograms per liter, respectively) and this has been repeated in reverse regarding the 59th rank (maximum pollution rate), which indicates the reliability of the rankings and the high accuracy of the proposed method.

The results showed that the pollution is concentrated in the central plains and partly in the North and Northwest plain due to the significant polluting industries, pesticides, and manure storage depot in these areas. Based on proposed approach can be calculated the rank pollution of groundwater quality samples with high accuracy, especially in cases where the assessment of water quality for drinking purposes must be considered.

The pressure on groundwater resources greatly increased with increase of human activities. Since the aquifers are majority drinking water supply, the correct evaluations of these resources are necessary. Due to the uncertainty of water quality standard ranges, a method that leads to reliable results can be valuable. So in this study, two approaches as EWOV and SPA was proposed in order to groundwater quality assessment using fuzzy concepts and entropy theory.

For qualitative evaluation of groundwater resources, two proposed approach are considered. In the EWOV method based on Euclidean distance between each parameter and their standard, the quality ratings of each sample is determined using certain quality ranges. In the SPA method, the samples are evaluated based on fuzzy interval of each parameter. To evaluate the effectiveness of the two approaches, the 21 samples of SARAYAN plain were used.

With implementation of the proposed approachs and extract the results of quality rankings samples can be found that the 42 percentage of the samples in wet period were in the mid-range class using EWOV method. By applying the uncertainty in quality classes, these percentages of monitored samples have acceptable quality level. Samples quality rankings study due to its location in the plains shows the results of SPA are more coordination with natural conditions of the aquifer. The results of the SPA method imply that the parts of the northwest, north and mid-plains are good and have acceptable quality for drinking consumption. Also, 42 and 74 percent of samples are located in quality classification of acceptable and acceptable to moderate in the wet and dry seasons, respectively.
Discussion and

Comparison of two methods result are indicative the appropriate precision of quality ranking offered by the SPA method and being consistent with the hydrogeological characteristics of the aquifer (such as P11 and P15). So, SPA as a preferred method in quality assessment of groundwater is recommended.

The proper prediction of reservoirs water level variation is considered as one of the important issues for management, designing, operation of dams and water supply. In this study, based on five soft models such as support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and the combined use of their results as input to one of these five models, a new structure called supervised intelligent committee machine (SICM) was proposed to predict the monthly reservoir water level of Karaj Amirkabir dam. The data used in this paper are water level, precipitation, evaporation, inflow and outflow of the dam. The evaluation of these models was done by nine error indexes and also the best model between them was selected using vikor decision maker method. After performing the necessary evaluations among the used soft models, the ANN known as the best model with nash–sutcliffe efficiency (NS) and mean square error (MSE) equal to 0.89 and 23.37 square meters, respectively. The results of the proposed approach are shown that the supervised (hybrid) neural network (SICM-ANN) has been able to provide high performance in predicting the monthly reservoir water level in Karaj dam with increasing the NS coefficient to 0.94 and decreasing the MSE index to 12.85 square meters (more than 45 percent decrease). Accordingly, hybrid use of soft models can be effectively applied to significantly reduce the predicted error of water level rather than single models.

Nitrate is one of the most significant pollutants that most aquifers, including the Karaj aquifer, have suffered from it. Because the conventional method to investigate the hydrogeochemical processes in the aquifer is graphical ones, in order to evaluate the pollution of nitrate in the qualitative data of 86 wells in Karaj aquifer in 2013, the multivariate statistical methods has been carried out as a complementary method for understanding the factors affecting groundwater quality, pollution identification and classification of similar samples. In this regard, XLSTAT software is used to study the pollution of nitrate and its relationship to other chemical parameters of water, and the factors influencing it. The statistical analysis indicates that hierarchal clustering has led to the extraction of three clusters. The second and the third cluster samples have a higher concentration of nitrate than the first one. The results, based on the principal components analysis (PCA), also show that the nitrate parameter has the highest correlation with chlorine and the least adaptation with sodium and sulfate. On the other hand, based on the varimax rotation, the main components were summed up to two components. The first one is a geogenic factor, which is due to the effect of the material that forms aquifer and second is an anthropogenic factor that is due to human actions, especially sewage

Considering the importance of redesign of dams spillways to provide optimum dimensions, the hybrid meta-heuristic based approach of Particle Swarm Optimization (PSO), Gray Wolves (GWO) and Direct Search Optimization (DSO) were proposed. In this approach, the spillway volume, which indicates the amount of concrete and cost, is considered as the objective function and spillway height, number of cycles, apex length and angle of wall are considered as decision variables. By implementing the proposed model, based on the Ute dam labyrinth spillway data, the spillway optimal dimensions were determined and compared with the other studies result. The results represent the very good performance of the proposed approach in generating global optimal solution, and non-hybrid use of DSO algorithm is not recommended due to network size constraint, increased computational cost and failure to achieve optimal solution. The optimal dimensions led to concrete saving and reduction of costs to 64% compared to the existing dam design. Investigating the optimal dimension of trapezoidal labyrinth spillway indicates that increase the flow capacity of the spillway to 15760 cubic meters per second. Finally, the comparison of the optimal dimensions obtained with two trapezoidal and triangular sections showed that the optimal form for labyrinth spillway is triangular.

The nonlinear Muskingum model has a significant advantage as compared to the linear one due to the nonlinear relationship between storage and flow dishcrage. In this model, the correct estimation of the parameters is necessary to achieve the proper precision. Previous studies indicate that there are five nonlinear corrected models, which, with different optimization algorithms, tried to increase the prediction accuracy of output hydrographs. Due to the error in the output hydrograph of the previous models, in this study, a new structure of nonlinear Muskingum model was developed based on hybrid PSO and DSO algorithms. In this model (NL6 model) with eight parameters, the improvement coefficient γ are used. This coefficient takes less and more than one according to the number of peak discharge in the output hydrograph. By applying the proposed approach to the three types of input hydrograph and determining the optimal values of the parameters of the NL6 model, it shows that this model has a high accuracy in estimating the discharge values of the output hydrograph. The error reduction rate of the NL6 model based on SSQ and SAD indicators for multi-peak hydrographs is 53 and 35.6 percent compared to the last proposed model, respectively. So, this model can have a high performance in estimated flood routing hydrograph.

Variation of temperature in concrete dams has a direct effect on the thermo-elasticity properties and creep phenomenon of concrete. These variations also, the stress and strains from them should be evaluated as initial loading used in the stability analysis of the dam. In this study, ABAQUS three-dimensional finite element model used to determine the thermal response of a dam under the effect of air temperature variations, solar radiation and water behind of the dam. So, a structural meshing with tetrahedral element block was used that each element had 20 nodes with 6 degrees of freedom in which there was the ability to analyze the stress and displacement.
In this research, the air temperature in the format of average daily air temperature was introduced closely as a boundary condition in ABAQUS model. The water temperature is other boundary conditions that were estimated based on the method proposed by Bofang to predict the water temperature at different depths. Also, in this model, the water level as the average daily water level was applied. The amount of displacement and moment of rotation at the level of dam foundation, both essential as boundary conditions, were used as fixed support and equal to zero. The attenuation model of the concrete was considered using the concrete damage plasticity (CDP) criteria with the coefficient of 3%. The modeling of dam foundation was performed according to a ratio of bedrock modulus of elasticity to the modulus of elasticity of concrete dam and using cubic model. The acoustic element was used to reservoir modeling. This element is the best choice in the modeling of water behind of dam. In this study, the considered hypotheses in the analyzing the dam models were as follow: A) Analysis carried out in a time domain, in which the time range of solving problem would be considered from zero to the alternation time. In dynamic analysis (earthquake), the real-time would be entered into the equation's solution. But in the statical analysis, the alternation time would be considered virtual time, and depending on the amount, the time of performing the program could be lower or higher. The software needs this virtual time to solve the equations step by step in different time periods. Generally, to solve a non-linear statically complex problem such as the provided model, it is recommended to analyze these problems by explicit analyzer and it is suggested to consider the entire time of the analysis longer than the alternation time or natural frequency corresponds to the first figure of the vibration mode of problem. Since time or the necessary frequency period for analyzing arc dam commonly is between to 10 to 30 seconds in dynamical analysis; therefore, this time was considered from zero to 20 seconds in the present study. B) Constituent material of the dam was linear, homogenous and viscoelastic.
The results showed that by thermal modeling, the final displacement direction was toward the reservoir while without thermal analysis and only by hydrostatic force modeling, the hydrostatic pressure tended to displace the dam in the opposite direction. This indicated the effect of thermal changes on the dam body that not only removed the effect of hydrostatic force but also it displaced the body toward the upstream reservoir. Also, the maximum displacement occurred during the warmest month of July and toward the reservoir. Therefore, due to high solar radiation, the slope of the heat changes in areas near the dam’s crest and the downstream areas exposed to sunlight were at high level. The dam body displacements toward the downstream in hot months were more than cold months. This was due to the simultaneous displacements caused by the thermal and hydrostatic loads in cold months. The results of analyzing finite element in different 12 months showed that upstream tensile stress was at low level and as a result, the possibility of cracking due to the static loads is excluded in this area. Based on the average level of the reservoir in 2009, it could be expected that as the water level decrease under the minimum level of the safety operation, the stress level would move from pressure to tensile state and increase the level of the main stresses in the dam body. So the effect of temperature on the creating critical states was assessed more than the effect of the water pressure.
The results obtained from model parameters such as displacement of dam crest, depth of cracked areas and analyzing temperature variation between different points indicated the high ability of this method for evaluation of the stability of the dam. The analysis showed that the maximum temperature and temperature gradient happen in the shallow area of the downstream level. Also, the temperature difference between different points of the downstream level and the same level point was considerable because of the effects of solar radiation. This could reveal that a lack of attention to operation of dam would lead to the occurrence of instability and cracks in whole arc dam body. The result of the proposed model showed that developed methodology had suitable accuracy in prediction of dam thermal behavior under the environmental actions.

Industrial development, increasing demand for water supply and illegal waste discharge into the aquifers have created major problems in most of the plains. Among the pollutants, heavy metals are considered as common factors due to their wide application in industry and urban communities. Thus, Zanjan plain as the hub of polluting industries is not exempted and the qualitative samples taken from it indicate the presence of different kinds of heavy metals. To investigate the role of natural and artificial factors in creation these pollutants, the used methodology is proposed based on geology, hydro-geochemistry study, the relationship between cations and anions and the distribution of heavy metals.
Using the proposed framework for measuring water quality data in both wet and dry periods, initially, all the factors leading to pollution were identified. Then, the role of anthropogenic and geogenic activities was determined based on a combination of factors involved in pollution of the plain.
Findings: The results indicate that the main part of contamination in the northern half of the plain is due to anthropogenic sources and appears naturally in the aquifer. In the central part of the aquifer, due to the centralization of industry and presence of Zanjan city, geogenic activities were identified to be the source of heavy metals. In other parts, both anthropogenic and geogenic activities were jointly involved in aquifer pollution.
The main source of pollution by heavy metals including Cr, Cu, Fe and Pb in Zanjan aquifer have been natural and geogenic factors. Also, the elements such as Zn, Mn and Cd are mainly associated with human activities and industries that are scattered throughout the plain. According to the evaluations, management strategies can be presented for restoration and control of pollutants input into the aquifer in that part of the plain whose contamination is due to geogenic activities.

Population growth causes an increase in different sectors water use. This increase requires new strategies for optimal water resources management. In this research, a three objective model was developed for maximization of economic benefit, maximization of reliable water supply and minimization of water withdraw from aquifers. In proposed model, aquifer and reservoir were considered as elements of water resources. Based on allocated water, water requirements, maximum yield of each product, production function were defined and economic benefits of regions in study area were determined. Using NSGA-II algorithm, Pareto trade-off curves for objective with various scenarios of cropping, economic benefit functions in Zarinehrood basin were derived. Since the points on trade-off curve cannot fully satisfy three objectives, optimal point was selected using Borda aggregation method and integration of five multicriteria decision models’ results. Result of comparison between current and optimal allocation demonstrates that we gained 42.5% water save by applying proposed model. In addition, comparison of current and optimal economic benefit showed that the benefit increased from 1,137,730 billion IRR to 1,285,971 billion IRR (13% increase). The result of this research can be used in other basins for decreasing in agricultural water use from water resources and increasing farmers’ income by optimal cropping pattern.

So far, many studies have been done to design reshaping berm breakwaters (RBB) that mostly have provided the effect of individual parameter on structure stability. Accordingly, the presentation of systematic optimal geometrical design of RBB is necessary. In this research, a generic program is presented to determine the optimal geometrical structure of RBB based on administrative costs. So, initially, the methodology were proposed to determine the breakwater optimal dimensions based on calculated berm recession from empirical formulae of previous studies and the cost of extraction and transportation of the materials from JAHAD and TOUSEH mines. The nominal diameter of stone blocks and the berm height from sea bed were selected as a decision variable based on sensitivity analysis. The results of implementation of the proposed structure using random search algorithm represent a significant reduction in costs of breakwaters construction compared to non-optimal values of decision variables. Based on the resulting optimal values, were determined the optimal percentages of extraction from miscellaneous mines with lowest extraction and transportation costs. The results indicated that the minimum cost is obtained if the values of nominal diameter of stone blocks and the berm height from sea be are equal to 0.82 and 4.9 meters, respectively. Therefore, the optimal width of berm recession was determined equivalent to 17.77 meters. Also, in order to achieve an optimal economic plan, the breakwaters constituent materials in JAHAD and TOUSEH mines must have 63.37 percentage with gradation of 1-2.5 tons and 61.71 percentage with gradation of 0.3-1.5 tons, respectively.

According to the nonlinear relationship between the components of the aquifer, these systems are called as a complex system. In these systems due to natural and artificial changes, the estimating the degree of complexity for quality and quantity groundwater assessment is very important. In this study, in order to ascertain impacts of SAVEH dam on the complexity of the SAVEH aquifer plain, which is located downstream of the dam, multi-scale entropy (MSE) theory was applied to analyze long-term variation piezometers (1985-2014). In this approach, unlike the sample entropy that only the amount of entropy for a single scale factor measured, the MSE used to assess the possible changes in the complexity of the aquifer in multi scale factor. The results show that the complexity of the aquifer after SAVEH dam construction precipitously decreased from the beginning to the middle of the aquifer plain and an average 31 percent increase can be seen in the difference between the sample entropy (complexity) in two periods (before and after the dam construction). The complexity properties alterations of the aquifer caused the destruction of natural systems aquifer and decrease the complexity of it. The comparison between proposed approach and Mann-Kendall method is indicated that the efficiency of MSE in evaluating the complexity of aquifer systems and detection of abnormal events in the groundwater table level time series. This study could provide references for conservation of the aquifer natural environment and the assessment of impacts induced by big hydraulic structures construction on the groundwater system.

The breakwater as structures that have an important role in the protection of coastal facilities was used to create calm in the harbor, reduce wave energy and also protect the coast against wave attack. Wave overtopping is an important parameter to design breakwater. In this paper, a new formula will be proposed to estimate wave overtopping on the reshaping breakwater. In this research, further effective parameters such as stone diameter (Dn50) and water depth at toe of the structure (d) were considered to estimate wave overtopping. Wave overtopping occurs when water level goes higher than the crest of the structure. To derive a new formula, at first a relation was proposed using hydraulic and structural parameters associated with wave overtopping and then the dimensionless parameter was used by Buckingham theory. A comparison was made between the new relation and the relations proposed by former studies. This research indicated that the proposed formula is accurate in estimating wave overtopping on reshaping berm breakwater.

The optimal design of water conveyance canal sections is one of the guidelines in management of water resources. Design of canal section with minimum loss of water is to consider minimizing seepage and evaporation under uniform flow condition. So far, the optimal design of canal sections was based on inaccurate estimates of seepage rate which needs to be revised. In this study, the seepage from a canal in miscellenouse conditions was modeled using SEEP/W software. The accurate soft model between input and output data was then extracted. Comparing the extracted soft model with other empirical equation, indicate the high accuracy of the selected soft model. This model were then used in optimization process with the aim of minimizing the canal water loss. The results are presented in terms of dimensionless graphs which made a simple design of canal dimensions possible. Comparing the results with other similar studies indicated the significant changes in optimal canal dimension.

The increasing demand for water in developed regions has led to numerous water supply challenges. Given their sensitivity and strategic importance as zones of cargo and passenger transfer, international airports are no exception to this general rule. The potential dangers to ground subsidence on the runway as a result of excessive groundwater withdrawal warrant plans aimed at developing models for optimal groundwater withdrawal as a measure of sustainable use of aquifers. In this study, consumption and withdrawal data are used and future development plans of the Imam Khomeini International Airport are considered to develop a model for groundwater use within the region based on multi-objective programming. Considering the objectives of maximizing water supply and minimizing costs of water transmission to demand zones, the model was used to develop an optimal water allocation system for the region containing monthly groundwater withdrawal policies and graphical distribution plans. The results show that the operation instructions thus developed for each well need to be carefully executed in order to ensure its safe exploitation. Strict adherence to the instructions is expected to enhance water supply reliability and to reduce the associated costs. This approach can be tailored and scaled up for future planning horizons and is also replicable in other areas by developing the relevant objective functions and constraints.

The decision to avoid a gradual salinization and swamping requires understanding the origin and the extent of salinity as well as quantity and up-to-date information of the area. Processing and interpretation of imagery satellite and regional analysis of statistical data such as electrical conductivity and groundwater table level variation can be used in recognition the salinity and swamp lands. In this study, with generating the historic and existing land use map and quantitative analysis of salinity index parameters, the effectiveness of the remote sensing using satellite landsat TM and ETM+ sensors from the summer of 1992 and 2002 was illustrated. This research also showed the quantitative variation in salinity and swamp land surface of BEHSHAHR plain in the period of 1986-2002. The result showed an increase in the saline land area and lands with drawdown in the groundwater table level for the expressed period. The results were consistent with the analysis based on the results of the interpretation of satellite images such as increase in classes of salty land use, rainfed–salty land use, decrease in irrigated agricultural and orchards, and increase in the rainfed farming lands.

Sudden contaminant load in water resources either man made or in circumstantial process causes crisis. Appearance of MTBE contaminant load in Geshlagh Dam in Sanandaj city, entering the sudden contaminant load of oil determinatives into Zayandehrood River and Karkhe River are examples of such crisis that have occurred in Iran. The purpose of this study is evaluating and simulation of reservoir quality behavior in case of sudden contaminant load. This can help the managers and planners in selecting the applicable policies in the situation of quality crisis. In this paper, Karaj Dam reservoir simulation model has been created by the third version of two-dimensional mean transverse (CE-QUAL-W2) and a biological contaminant with coliform index has been considered as a sudden contaminant load. By considering the factors affecting the behavior of advection, dispersion, diffusion, and deterioration of coliform, five different scenarios have been created for 12 months of the studied year. These included coliform deterioration rate, the effect of reservoir water storage volume, contaminant load input location, the effect of water temperature, discharge into the reservoir, and the coliform sediment rate. Based on the defined scenarios, the rate of influences of each abovementioned factor on the behavior of pollutant has been investigated. The result shows that the input location of the contaminant load has a severe effect on the maximum output contaminant and crisis period. Therefore entering the contaminant load at the lower third of reservoir comparing to those of at the upper third of the reservoir has 15 times more output contaminant and the crisis period due to entering the contaminant load at the lower one third of the reservoir is two times of the crisis period due to entering the contaminant load at the upper one third of the reservoir.

Simulation of behavior of aquifers into determining groundwater exploitation rate and planning for sustainable use of groundwater is very important. In this regard, groundwater models using hydraulic, hydrological and hydro-geological parameters were developed to predict the quality and quantity of aquifer parameters. Good knowledge of these parameters can be increase the accuracy of aquifer simulation. For this purpose, a structure based on advance optimization methods for calibration of parameters with low accuracy (coefficient of hydrodynamic, aquifer recharge and withdrawal rates) were developed. In the proposed model, minimizing the sum of the square differences between simulated and observed groundwater level was considered as the objective function. Allowable range of calibration parameters has been considered as constraints. The governing flow equations of the aquifer used to determine the groundwater level in each cells. These equations were solved by the finite difference method. by runing the proposed model, the optimal values of parameters were determined in each cell. The comparison between simulated and observed groundwater level shows the capability of the proposed approach in accuratey estimation of the aquifer parameters.

Flooding and its damages are not only found uplift water level in a region. In other words, the depth and speed parameters together have determining the level of flood risk at each point. This subject is visible in flooded plain with low height and high speed of 2 meters per second, which damages are extensive. According to the criteria of having both velocity and flow depth in the governing equation to the flows energy, this equation seems appropriate to analysis in this study. Various methods have been proposed for increase accuracy in flood zoning with different return periods and risks associated with it in land border of river. For example, some of these methods are considered factors such as analysis of past flooding in the area affected by floods, hydrological factors and consideration of hydraulic elements affecting in flood zoning (such as flow velocity). This paper investigates the effect of flood zoning by the energy flow in the areas affected by floods. Also risk due to flood based on energy flow in each section of the river is compared by the proposed graphs of hazard interval and other done flood zoning in this field. In this study, the FORDO river has been selected as the case study. This river is part of the rivers located in the city of QOM KAHAK. The characteristics of river in upstream and downstream are mountain, young and stable and adult, respectively. Also this river in different seasons is exposed the flood damage. The proposed method in this study can be improving recognition accuracy of flood risk in areas affected by flood. Also, this method facilitate the identify parts of the river bed, that is affected by severe flooding, for decision making to improve rivers organizing.

The aim of this study is the spatial prediction runoff using hydrometric and meteorological stations data. The research shows that usually there is a certain communication between the meteorological and hydrometric data of upstream basin and runoff rates in output basin. So, if can be extracted the rules related to historical data that recorded at stations, can be easily predicted runoff amount based on data measured. Accordingly, among the tools available, the fuzzy theory (with flexibility in developing fuzzy rules) can be provide the knowledge lies in the observed data to parameters prediction in real time. So, in this research the fuzzy inference system has been used for estimating runoff rates at stations located in the Taleghan river downstream using rain gage stations and hydrometric stations upstream. Because the inappropriate values associated with membership functions, the fuzzy system model can not provide correct value for the prediction. In this study, a combination of intelligence-based optimization algorithm and fuzzy theory developed to accelerate and improve modeling. The result of proposed model, optimum values to each membership function that related to dependent and independent variable extracted and based on it’s the runoff rates in rivers downstream predicted. The results of this study were shown that the high accuracy of proposed model compared with fuzzy inference system. Also based on proposed model can be more accurately the rate of runoff estimated for future conditions.

In the most of arid and semi-arid countries، the quality and quantity of available water resources play a significantly limiting role in development. In these regions، the conjunctive management is a suitable alternative that can lead to the optimal operation of available water resources. In this study، a conjunctive use model is developed in order to maximize water supply demands in 30-year period historical records. Discharge from resources، consumption priority in sectors and zones، inter-basin water conveyance plans and water demands are considered as the optimization model’s constraints. The ultimate goal of the study is to present short-term، medium-term and long-term operating policy. The sequential genetic algorithm is used to speed up the convergence to near global solution. The operating rules of surface and groundwater resources are separately developed for agriculture as well as domestic demands in the annual and monthly manner، respectively in the three above-mentioned periods. Finally، these rules are verified based on previously unused historical records. The results showed that the proposed model can effectively improve water resources potential، sustainable groundwater resources and undesirable use in agriculture sector.

Changes due to the utilization of the river lead to changes in river morphology and the impact on the river of life, environment and ecology of the river affects. To maximize the use of flood plain and it protection is the construction of structures to suit the type of land use expected. This type of action often leads to changes in the environment. This study using the hydraulic parameters, the parameters of the integrated flood management related to environmental and interaction between flood and land use along the river to assess the riparian and risk of flooding in the river.The analytical hierarchical process (AHP) was chosen as the method for prioritizing selected management alternative. Also, after combining the results of flood river flow hydraulic model and AHP model developed, river riparian risk zoning was determined by the environmental approach. In this study, the DAREHBAGH River of the rivers of KAHAK of QOM province has been selected as the case study. This river along during the course of its have been violated and the recent flooding in the DAREHBAGH river is leading to environment threats.

In water supply systems, the accidents occurring in pipes are of the utmost importance and sensitivity. Failure of the pipes is not necessary with the end of their life and different factors namely age, diameter, material, stability and corrosion of soil and water, execution, installation and operational conditions such as hydraulic pressure are effective on it. At the same time studies show non comprehensiveness of presented relations in prediction of pipes failure rate. In this research, with regards to the available software and hardware, a structure was developed using combination of new optimization and simulation models. In this structure, theartificial neural networks were used for simulation of the pipes failure rate. Considering the point that neural networks are always consider as a black box and unable to provide the effect of each independent variable on the dependent variable, on the other hand, they are prone to incorrect training. Therefore, in this study to determine the input parameters affecting failure rate and the most appropriate structure of artificial neural network (as well as bios vectors, layers adjusting weights and the number of neurons), Genetic algorithm has been used with the aim of presentation of a structure which has the minimum error rate of simulation.In this algorithm, decision variables and properties of neural networks are the parameters affecting the failure rate. By running the developed optimization model, in addition to the effective parameters on pipe failure, the best neural network structure for simulation pipe failure rate can be determined. The advantage of the proposed method is full coordination between the input parameters and network structure in prediction of the pipes failure rate. The results of this study can be used to find the most appropriate relationship failure rate pipes with regards to the effective parameters and take necessary actions for decision making lead to resolve problems due to it. The results of this research indicating that the proposed combination method is able to extract the optimal and effective parameters on the pipes failure rate amongst the factors affecting failure rate, and also have been caused improve power capabilities and expansion of neural network structure that indicate high efficiency of the proposed method in simulation of nonlinear and complex relations.