جستجوی مقالات مرتبط با کلیدواژه « الگوریتم ازدحام ذرات » در نشریات گروه « آب و خاک »

Prediction of effluent quality parameters of wastewater treatment plant is essential in managing water resources, limitation of fresh water resources in the world, furthermore the ever-increasing population growth and the development of urbanization, have made the approach of urban wastewater reuse inevitable. In such a situation, the use of recycled water can be considered as one of the ways to overcome water shortage and prevent wastage of water resources. This research aimed to investigate the performance of Prediction of effluent quality parameters of wastewater treatment plant in using recycled water. On the other hand, due to the health hazards caused by the discharge of wastewater from wastewater treatment plants to water sources, achieving a precise design and correct management of wastewater treatment plants (WWTP: Wastewater Treatment Plant) is one of the important challenges of sustainable water resources management. 4 effective parameters including (BOD), (COD), (TSS) and (pH) of wastewater, were selected as input to the model, during a statistical period of 3 years (1397 to 1399). Also, 70% and 30% sizes were determined as the best sizes for training and testing stages in order to model BODeff and CODeff parameters. In this study, multi-Layer perceptron models (MLP), basic radial neural network models (RABF), as well as the integration of these models with several other algorithms, such as genetic algorithm (GA), particle swarm optimization algorithm (PSO) and sine cosine algorithm (SCA), were used in order to predict the quality parameters of wastewater treatment plant effluent.

Cropping pattern optimization is an important strategy in the optimal use of water in agriculture. The present study attempted to develop and evaluate four hybrid models based on maximizing economic return rate and minimizing risk based on risk indices including variance (MVar model), semi-variance (MSVar model), absolute value deviation from mean (MADev model) and conditional value at risk (MCOVaR model). The MCOVaR model is governed by observing the rules, which at a selective confidence level allows determining the best cropping pattern scenario with selective return rate and the lowest risk that can be measured in the decision-making stage. The data used are related to the downstream irrigation network of Ardabil Yamchi Dam and for the 1397-98 crop year. The development of the model in MATLAB is based on nonlinear programming and optimization problem solving using particle swarm algorithm in a multi-objective solution. The highest correlation coefficient in regression of the models with Fourier equation was R2=0.9997 with accuracy (RMSE=0.0011) for MADev model, The results showed that the highest yield of 0.31 belonged to potatoes with a maximum risk of 58% and the lowest yield of 0.13 belonged to the maize plant with a maximum risk of 63%. By calculating 50 scenarios of the best possible cropping patterns in terms of satisfying the target functions and governing conditions, the pareto front of each model was drawn and the table of system income values for risk levels of 20% and 30% was extracted and presented. The results obtained in all models indicate an increase in economic efficiency by increasing the risk level of the system in selecting the optimal cropping pattern and this increase in MADev model is more steep than other models and increasing risk-taking leads to increasing the area under cultivation of plants with more water consumption.

Water scarcity is one of the problems to the utilization of water resources in the agriculture sector. Therefore, a principled management of water resources is needed to optimally meet the water requirements of this sector. The Irrigation water allocation management could play an important role in confronting the water scarcity of the agriculture sector. Use of optimization technique to optimally allocate water and land has recently been proposed and used in many studies and it was proved to be very effective in the optimal water allocation problems. Rabie et al. (2015) allocated water to the lands irrigated by Ordibehesht canal of Doroodzan irrigation network in Fars Province using genetic algorithm. Results indicated that total cultivated area could be reduced by up to 12% under optimal water allocation situation. Results of the study conducted by Mizaei et al. (2017) on optimizing the cropping pattern of Golestan irrigation network using genetic algorithm demonstrated that 38% of the available water left as the surplus water after the optimization. This amount of water could increase the cropping area by 1388 hectares. In this study, an optimization model was created to optimally allocate the available water to Maroon irrigation network which is located in Khuzestan province of Iran using the genetic algorithms optimization method with the purpose of maximizing the total net benefit.

First, the water requirement of all crops in the Maroon irrigation network was calculated according to Allen et al. (1998) using Cropwat 8.0 software. Then, an optimization model was created to optimally allocate the irrigation water to the cropping pattern of the Maroon irrigation network using Matlab software. In this model, the water year was divided into 36 periods consisting of 10 days. The amount of irrigation water depth of the crops and their cropping area were considered as the decision making variables of the model. As Genetic algorithm has been proved to be effective in the recent optimal irrigation water allocation and cropping pattern studies, the genetic algorithm optimization method was used in the model to allocate irrigation water to the Maroon irrigation network with the goal of maximizing total net benefit (Faghihi et al., 2015; Rabie et al., 2015; Mirzaei et al., 2017). Furthermore, particle swarm optimization method was used in order to verify the results obtained by genetic algorithm optimization method. Comparison of the results obtained using the mentioned optimization methods can demonstrate whether the genetic algorithm results are verified or not. In order to achieve the optimal solution, both of the optimization methods had some parameters to be set. The parameters were set using the Vikor index according to Akbaripour and Masehian (2013).

Total net benefit maximization results obtained by particle swarm optimization method verified the maximization results of total net benefit obtained by the study model using genetic algorithm optimization method. The model Results indicated that the cropping pattern of all crops is increased except alfalfa and wheat. Total cultivated area of the network is increased by 1271 hectares which means that 14% of the abandoned area of the network could be recultivated. There are so many lands that were cultivated in the past and is not currently under cultivation because the available water resources cannot meet all of the water requirements of the agriculture sector in the study area. So, some of lands were abandoned and is not under cultivation anymore, but the optimal allocation of irrigation water is an efficient strategy to handle the water scarcity challenge in the agricultural sector and some of these lands could be cultivated again which leads to a more efficient agriculture sector. Furthermore, the irrigation depth was reduced in all of the crops, but this was compensated by the model using the available soil moisture. So, the crops were exposed to insignificant amount of water stress and the crops yield is not significantly reduced, however, the amount of total water use is not significantly reduced in comparison to the current irrigation water allocation situation because the network total cropping area is increased. The water resources allocation optimization lead to increase in total net benefit by 74.9 billion Rials. Results also proved the efficiency of genetic algorithm in this water allocation optimization model by increasing the total cropping area and the network total net benefit.

Monitoring network optimization is a decision making process for the best combination of available stations. Due to economic considerations and reduction of monitoring costs ، the optimization approach in this study is to reduce monitoring stations without reducing the amount and accuracy of the information obtained. In this study, an optimal design of groundwater quality monitoring network was carried out with the help of an optimization model in the Neishabour plain aquifer. The optimization of the wells network was accomplished by a Multi Objective Particle Swarm Optimization (MOPSO) algorithm. Two objectives containing of minimizing the root mean square error (RMSE) and the number of wells was applied in current research. Kriging interpolation was used for calculating groundwater chlorine concentration values and compared with observation values. As a result of this research was presented a Pareto front exctracted from MOPSO showing the number of wells against their corresponding RMSE, which could be a guide for the design of a groundwater quality monitoring network. The outcome showed that the sampling wells can be reduced to 58 percent with a minimum error increase (all 50 wells in base network with zero error may be reduced to 21 with chlorine concentration error of 13.57 mg/l) in the Neishabour aquifer. Also, the position of these wells was considered as the optimal position.

Decreasing the accessible water that is because of the natural effects such as warming the earth, population daily increase and increasing of the water requirement have caused that water allocation become one of the most important problems. This issue especially in Transboundary Rivers which pass from several countries also produces the political problems. Therefore, cross-border water resources management is imperative. One of the strategies for water resources management is optimization of resources allocation. In boundary watershed that has several beneficiaries, it is necessary to interest to the satisfaction of all the stakeholders to prevent the occurrence of conflicts between them. To achieve these goals can be use from methods of dispute resolution and optimization of water resources allocation. In this study, the Aras Border River is investigated. To solve the water problems in the basin use from subset of bankruptcy dispute resolution procedure simultaneously the particle group algorithm (PSO). The results show the superiority of Constrained Equal Loss method that results the more average of allocation for total needs. Considering that the Aras watershed is border In the process of allocation of water in the basin should be fitted to the consent of all the stakeholders to prevent of conflict between them. To solve the water issues in the basin use of the bankruptcy dispute resolution method with the optimum particle group algorithm in the allocation of resources. The results show the superiority of Constrained Equal Loss method that result more average allocation for total needs of basin. But in this way the applicant with the least need that is Turkey in this basin, be fitted in last priority. Given that this country is located in the upstream basin, apply this method in this area need to realization of alternative facilities for Turkey and satisfying the stakeholders in this country.

Regional flood frequency studies are initialized by the delineation of the homogeneous catchments. This study was based on "Region of Influence" concept, aiming to find the similar catchments in the south of Caspian Sea. The methodology utilized the Particle Swarm Optimization Algorithm, PSO, to optimize the fuzzy system over a dataset of catchment properties. The main catchment variables in relation to flood were determined by the principle component analysis method and employed as the inputs in the fuzzy system. Catchments grouping was performed over these fuzzy input variables by the iterative process. The optimum similar groups were obtained by PSO, and the heterogeneous L-moment index was used as the termination criterion for the optimization process. A total of 61 hydrometric stations located in the study area were selected and their relevant catchments' physical, climatic and hydrologic properties in relation to flood were studied. Principle Component Analysis by Variomax Rotation Factor over the catchments datasets tended to four out of 16 physical variables, including area, mean elevation, Gravelious Factor and Form Factor, as the main parameters in terms of homogeneity with 84 percent of accumulative variance. These variables, as well as mean annual rainfall, were used as the input data to define the fuzzy system. PSO algorithm was then employed to optimize the developed fuzzy system. The developed algorithm tended to yield the best result in the 9th iteration with 26 and 22 for the minimum average and the optimum values of cost function, respectively. The topology of the resulting algorithm included inertia weight, local and acceleration rates, the number of generations and population size, with the values of 0.7298, 1.4962, 1.4962, 10 and 5, respectively. This study tended to a total of 61 regions of influence, proportional to the relevant 61 sites. According to the geographical location of the catchments in the region, it could be concluded that the geographical proximity doesn't necessarily involve homogeneity. The obtained results indicated the efficient potential of PSO-FES in the delineation of the homogenous catchments in the study area.

Due to the limited surface water resources and reservoir capacity of dams, scientific management and optimal utilization policy of reservoirs is essential and vital in meeting the water requirements. Considering the importance and utilization of water resources and the optimal allocation of this scarce resource among different uses in the Sistan region, it seems necessary to establish a plan to achieve this goal. In the studied area and in the country, so far, an investigation into the optimal allocation of water with the application of advanced PSO algorithm for optimization of semi-Sistan reservoirs has not been used. Therefore, in the present study, the optimal allocation of water resources in the Chah-Nimeh reservoirs under three management scenarios (scenario of stabilization of micro-organisms, agricultural development and transfer of second drinking water from reservoirs to the district of Zahedan) using the metamorphic technique of congestion Particles (PSO) have been investigated. particle swarm optimization method or abbreviated mass optimization (PSO) is a population based stochastic optimization algorithm that simulates the social behavior of birds Group is inspired. In the mass optimization algorithm of particles, there are organisms that they are called particle and are spilled in the search space of a function that is intended to minimize (or optimize) its value. Each particle calculates the value of the target function in the position of the space in which it is located. In this research, the algorithm was used to allocate optimal water resources. The objective function in this research is to optimize and maximize the amount of water supply. Also, the constraints are related to the objective function, the systematic constraints, the constraints and limitations of the algorithm and the constraints and limitations of the reservoirs in the region. According to the results, the optimum release rate in 1995 (first year) was 39.35 million cubic meters, with the demand of 98.31 million cubic meters, which did not meet the required amount of 72.91 million cubic meters. The comparison of the last four years shows that in the year 29, lack of supply was less than the next three years. The scenario of stabilization of the microstats in the study area was considered as a serious project. The results of this scenario showed that the application of the algorithm used can properly optimize the allocation of water resources. According to the results, it is suggested that ultra-modern modeling can achieve more optimal allocation with the minimum error in the target function.

Exploitation of dam reservoirs is one of the major problems in the management of water resources. In this research, Crow Search Algorithm (CSA) was used for the first time to manage the operation of reservoirs. Also, the results related to the exploitation of the single-reservoir system of Shahid-Rajaei dam, located in Mazandaran province, northern Iran, which meets the downstream water demands, were compared to those obtained by applying the Particle Swarm and Genetic algorithms. Time reliability, volume reliability, vulnerability and reversibility indices, and a multi-criteria decision-making model were used to select the best algorithm. The results showed that the CSA obtained results close to the problem’s absolute optimal response, such that the average responses in the Crow, Particle Swarm and Genetic Algorithms were 99, 75 and 61 percent of the absolute optimal response, respectively. Besides, except for the time reliability index, the CSA had a better performance in the rest of the indices, as compared to Particle Swarm and Genetic Algorithms. The coefficient of variation of the obtained responses by CSA was 14 and 16 times smaller than the Genetic and Particle Swarm Algorithms, respectively. The multi-criteria decision-making model revealed that the CSA was ranked first, as compared to the other two algorithms, in the Shahid-Rajaei Reservoir's operation problem.

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.

In this paper presented the evaluation of particle swarm optimization for solving complex optimization water resources problems. The main problem with PSO is it’s prematurity. Therefore a new adjustable PSO-GA hybrid algorithm which combines PSO with genetic operators was proposed. The basis behind this is that such a hybrid approach is expected to have merits of PSO with those of GA. The main idea of GA is due to its genetic operators crossover and mutation. By applying crossover operation, information can be swapped between two particles to have the ability of flying to the new search area. The purpose of applying mutation to PSO is to increase the diversity of the population. For evaluating of the proposed algorithm the optimization of the hydropower operation of ‘‘Dez” single reservoir has been studied. The results of HPSOGA compare to PSO and GA indicated the proposed algorithm increases the flexibility and capability of PSO to generate strong-developing individuals that can achieve faster convergence rate to optimum point and it is very useful in solving optimization operation water resources.