Hamid Reza Moradi; khalil Jalili; Omid Bozorg Hadad
Abstract
Introduction: The conflict between environmental protection and the economic development by different land uses within a watershed are challenges facing land use planners in many developing countries. Because of the growing demand for water, water resources optimization allocation management is at the ...
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Introduction: The conflict between environmental protection and the economic development by different land uses within a watershed are challenges facing land use planners in many developing countries. Because of the growing demand for water, water resources optimization allocation management is at the forefront in formulating sustainable development policies for many countries. Conjunctive use of surface water and groundwater is being practiced in many regions of the world to bring more areas under irrigation, increase agricultural production and productivity, and also maintain overall system balance. Successful agricultural water management policies put the physical, hydro-geological, and socio-economic constraints on these integrated water supplies. Application of optimization approaches has been started since the human faced a low efficiency production of the system. Optimization of resource allocation is one of the proper strategies to achieve sustainable development and to reduce resource dissipation. So land and irrigation water allocation based on water balance approach is the aim of this research and this paper proposed an optimal land and water resource allocation model based on linear programming to Islamabad plain’s irrigation areas.
Materials and Methods: The Islamabad plain aquifer is located in Seymareh watershed and 55 km of Kermanshah city in the Kermanshah Province; it comprises 19438 ha and extends between 33◦20 to 34◦24 N latitude and 46◦ 15to 46◦47 E longitude. Annual precipitation and annual temperature of study area are 445.1 mm and 12 ◦C respectively . The mean net benefit of irrigated wheat, sugar beet, corn, potato, irrigated chick-pea, alfalfa, vegetables, melon, tomato, fruit garden, dry wheat, dry barley, dry chick-pea and dry lentil were therefore calculated to be respectively some 38.21, 76.7, 34.39, 81.0, 16.98, 21.69, 47.2, 12.4, 61.4, 74.0, 7.26, 0.72, 17.1 and 10.9 Mir/ha and the objective functions of the benefit maximization problem in the Islamabad aquifer was formulated The problem was structuredin the study area to maximize economic return. The information and data required for defining constants and coefficients of objective Function and constraints, viz. Land availability, water availability/supply, present crop pattern, socio-economic conditions were extracted from the available comprehensive Hydrogeology, field studies and farmers viewpoints. A linear optimization problem has been formulated for the Islamabad plain to achieve sustainable development and optimal land allocation to crop pattern, then solved using the simplex method with the help of LINGO software packages and the optimal solution was ultimately determined. Three management scenarios and six action plan with resources accessibility, crop rotation, socio-economic constraints and nonnegative variables have analyzed and sensitivity analysis was done.
Results and Discussion: The results of the study verified that the linear optimization problem was successfully solved using the LINGO software program and the results led to maximize benefits in the Islamabad plain. The results also showed the successful linkage between economic aspects and environmental outcomes at an aquifer scale. Results show that in all scenarios sugar beet, corn, chick-pea, tomato and melon have been removed from the optimal cropping pattern. Wheat areas in two scenarios and five action plans have been increased. Benefit of optimization in management scenarios and in the entire optimal crop pattern was positive and increase from 19 to 55 percent. Sensitivity analysis showed that the change of some specific allocations would create much more impact on the final optimal solutions generated by the optimization programming.The results of sensitivity analyses also showed that the objective function was strongly susceptible to the constraint of water availability and total area of plain.
Conclusions: A benefit problem was formulated and then solved to maximize benefits using optimization of allocable land and irrigation water resources to 14 productions of present crop pattern within the Islamabad plain in Kermanshah province. The LINGO optimization software program was successfully applied and led to determine appropriate areas allotted to different crop. The results obtained during the study approved the applicability of optimization model in solving problems which sometimes conflicting each other. On the study plain there appears a significant augmentation in profit from allocating the optimal cultivated areas. The approach could provide better information on where changes are required, how large the changes need to be, and how much the changes will benefit the people when improving. The conjunction of optimization techniques with other tools like geographical information system, genetic algorithm, fuzzy logic, artificial neuron networks and applying different softwares and simulation techniques are also suggested to be taken into account in further studies to draw ultimate necessary conclusions.
Shima Soleimani; Omid Bozorg Haddad; Mojtaba Moravej
Abstract
Introduction: Surface water bodies are the most easily available water resources. Increase use and waste water withdrawal of surface water causes drastic changes in surface water quality. Water quality, importance as the most vulnerable and important water supply resources is absolutely clear. Unfortunately, ...
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Introduction: Surface water bodies are the most easily available water resources. Increase use and waste water withdrawal of surface water causes drastic changes in surface water quality. Water quality, importance as the most vulnerable and important water supply resources is absolutely clear. Unfortunately, in the recent years because of city population increase, economical improvement, and industrial product increase, entry of pollutants to water bodies has been increased. According to that water quality parameters express physical, chemical, and biological water features. So the importance of water quality monitoring is necessary more than before. Each of various uses of water, such as agriculture, drinking, industry, and aquaculture needs the water with a special quality. In the other hand, the exact estimation of concentration of water quality parameter is significant.
Material and Methods: In this research, first two input variable models as selection methods (namely, correlation coefficient and principal component analysis) were applied to select the model inputs. Data processing is consisting of three steps, (1) data considering, (2) identification of input data which have efficient on output data, and (3) selecting the training and testing data. Genetic Algorithm-Least Square Support Vector Regression (GA-LSSVR) algorithm were developed to model the water quality parameters. In the LSSVR method is assumed that the relationship between input and output variables is nonlinear, but by using a nonlinear mapping relation can create a space which is named feature space in which relationship between input and output variables is defined linear. The developed algorithm is able to gain maximize the accuracy of the LSSVR method with auto LSSVR parameters. Genetic algorithm (GA) is one of evolutionary algorithm which automatically can find the optimum coefficient of Least Square Support Vector Regression (LSSVR). The GA-LSSVR algorithm was employed to model water quality parameters such as Na+, K+, Mg2+, So42-, Cl-, pH, Electric conductivity (EC) and total dissolved solids (TDS) in the Sefidrood River. For comparison the selected input variable methods coefficient of determination (R2), root mean square error (RMSE), and Nash-Sutcliff (NS) are applied.
Results and Discussion: According to Table 5, the results of the GA-LSSVR algorithm by using correlation coefficient and PCA methods approximately show similar results. About pH, EC, and TDS quality parameters, the results of PCA method have, the more accuracy, but the difference of RMSE between the PCA method and correlation coefficient method is not significant. The PCA method cause improvement in NS values to 22 and 0.1 percentages in pH and TDS water quality parameters to the correlation coefficient method, respectively,and NS criteria value for EC water quality parameter did not change in both methods. As a result, according to positive values of NS criteria in both PCA and correlation methods, it is clear that GA-LSSVR has a high ability for modeling of water quality parameters. Because of summation of NS criteria for PCA method is 5.53 and for correlation coefficient is 5.62, we can say that the correlation coefficient method has more applicable as a data processing method, but both methods have a high ability. Orouji et all. (18) used assumed models to model Na+, K+, Mg2+, So42- , Cl- , pH, EC, and TDS by Genetic programming (GP) method. The RMSE criteria of the better models for testing data are 2.1, 0.02, 0.85, 0.93, 2.18, 0.33, 404.15, and 246.15, respectively. For comparison the orouji et al. (18) and table (5), the Results show using the correlation coefficient method as a data processing method can improve the results to 5.5 times. The results indicate the superiority of developingalgorithm increases the modeling accuracy. It is worth mentioning that according to NS criteria both selected inputs variable methods (correlation coefficient and PCA) are capable to model the water quality parameters. Also the result shows that using correlation coefficient method lead to more accurate results than PCA.
Conclusion: In this study, GA algorithm as one of the most applicable optimization algorithms in the different sciences was used to optimize the LSSVR coefficients and Then GA-LSSVR was developed to model the water quality parameters. To comparison data processing methods (correlation coefficient and PCA methods), the input variables of both methods were determined and GA-LSSVR was performed for each of the input variables. To compare the results of the PCA and correlation coefficient methods, some statistics were used. It is worth mentioning that according to NS criteria both input selection methods are capable to model water quality parameters. Also the results show that using correlation coefficient method lead to more accurate results than PCA.
Habib Akbari Alashti; O. Bozorg Haddad
Abstract
Considering the necessity of desirable operation of limited water resources and assuming the significant role of dams in controlling and consuming the surface waters, highlights the advantageous of suitable operation rules for optimal and sustainable operation of dams. This study investigates the hydroelectric ...
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Considering the necessity of desirable operation of limited water resources and assuming the significant role of dams in controlling and consuming the surface waters, highlights the advantageous of suitable operation rules for optimal and sustainable operation of dams. This study investigates the hydroelectric supply of a one-reservoir system of Karoon3 using nonlinear programming (NLP), genetic algorithm (GA), genetic programming (GP) and fixed length gen GP (FLGGP) in real-time operation of dam considering two approaches of static and dynamic operation rules. In static operation rule, only one rule curve is extracted for all months in a year whereas in dynamic operation rule, monthly rule curves (12 rules) are extracted for each month of a year. In addition, nonlinear decision rule (NLDR) curves are considered, and the total deficiency function as the target (objective) function have been used for evaluating the performance of each method and approach. Results show appropriate efficiency of GP and FLGGP methods in extracting operation rules in both approaches. Superiority of these methods to operation methods yielded by GA and NLP is 5%. Moreover, according to the results, it can be remarked that, FLGGP method is an alternative for GP method, whereas the GP method cannot be used due to its limitations. Comparison of two approaches of static and dynamic operation rules demonstrated the superiority of dynamic operation rule to static operation rule (about 10%) and therefore this method has more capabilities in real-time operation of the reservoirs systems.
O. Bozorg Haddad; S. Khosrowshahi; Mahboubeh Zarezadeh; P. Javan
Abstract
The man’s craving for water has inspired many civilizations to be formed near rivers. The social and economic destructive consequences of flood in human societies are considered undeniable facts. Today human trespasses on riversides and also vegetation destruction have caused increase in flood damages. ...
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The man’s craving for water has inspired many civilizations to be formed near rivers. The social and economic destructive consequences of flood in human societies are considered undeniable facts. Today human trespasses on riversides and also vegetation destruction have caused increase in flood damages. These factors lead to be not only vital and financial damages, but also damages such as soil erosion in upstream and soil deposition in downstream. This research aims to decrease flood damages using structural methods as well as investigating and finding proper locations to construct protective levees in high risk areas via studying torrent area of riversides. In this research, the Genetic Algorithm (GA) are applied to maximize the benefit of flood control and also to minimize the cost of protective levees construction. Therefore, the fitness function of the research is defined to maximize net benefit of the project. The objective of the present paper is to evaluate this method for decreasing flood damages in the “Sarm” and “Khoor Abad” rivers, located in Qom province in Iran. The proper location and height of levees are defined whether the factor of “the level of saved losses to the region by constructing protective levees minus the cost of constructing protective levees” is maximized. The results indicate that by constructing protective levees the rate of damages reduces up to 99% in comparison with a non-constructed protective levees scenario.
S. Beygi; O. Bozorghaddad; M. Khayatkholghi
Abstract
In recent years, limitation of water resources and the increasing trend of population growth cause the quantity issues on water supply. On the other hand, permanent and sudden pollution of waters as a biological threat has always been of high importance, because occurrence of biological pollution in ...
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In recent years, limitation of water resources and the increasing trend of population growth cause the quantity issues on water supply. On the other hand, permanent and sudden pollution of waters as a biological threat has always been of high importance, because occurrence of biological pollution in water systems produces national crisis. However, since there are various and contradictory goals and utilities in reservoir systems, making proper decisions considering all aspects is a complex issue. A good strategy to compromise between the contradictory goals and utilities would be conflict resolution methods. To perform this study the allocations of drinking and agricultural waters of the Karaj dam, as one of the most strategic dams in Iran in the case of quality attacks, has been used. In this study, Nash model has been used as the conflict resolution method. According to the results, when there is a decrease in the quality aspects of water, Nash model assigns the priority to the quantity ones and allocates low-quality water to the consumers. Thus, a new conflict resolution method has been developed so that the allocations are modified according to the quality aspects. Thus, low-quality water is less allocated while more allocation is made along with an increase in the quality utility.
M. Mohammadi Ghaleni; O. B ozorg Hadad; K. Ebrahimi
Abstract
Abstract
The Muskingum method is frequently used to route floods in Hydrology. However, application of the model is still difficult because of the parameter estimation’s. Recently, some of heuristic methods have been used in order to estimate the nonlinear Muskingum model. This paper presents a ...
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Abstract
The Muskingum method is frequently used to route floods in Hydrology. However, application of the model is still difficult because of the parameter estimation’s. Recently, some of heuristic methods have been used in order to estimate the nonlinear Muskingum model. This paper presents a efficient heuristic algorithm, Simulated Annealing, which has been used to estimate the three parameters nonlinear Muskingum model. The results show the high accuracy of the algorithm in estimation of the parameters, so that it is obtained terms of the sum of the square of the deviations between the observed and routed outflows (SSQ), the sum of the absolute value of the deviations between the observed and routed outflows (SAD), deviations of peak of routed and actual flows (DPO), and deviations of peak time of routed and actual outflow (DPOT), 36/78, 23/44, 0/9 and 0, respectively. As Value of the SSQ has obtained equal its value Harmony Search method that is the best answer between the heuristic Optimization Algorithms that has been used so far. Finally, the performance of the new proposed method has been compared with other methods. The results showed that the height efficiency of the algorithm in parameter optimization of the nonlinear Muskingum model. SA algorithm in the second example the Karun River flood test and the results were compared with the GA method. The results showed that SA algorithms estimate is better than the GA method. As the error sum of squares (SSQ) before 4947/06, the total absolute error (SAD) against 412/8, Dubai actual peak was 1182 cubic meters per second and peak Routing 1191 was obtained by the difference of these two (DPO) times less a percentage error and the occurrence of different steps in Dubai when the real peak and has Routing (DPOT) zero respectively. Finally, this research capability in the blank verses optimal SA algorithm making Muskingum model parameters indicated therefore, to use SA algorithm in this area is recommended.
Keywords: Flood Routing, Muskingum Model, Optimization, Simulated Annealing Algorithm
M. Zarezadeh; O. Bozorg Haddad
Abstract
Abstract
One of the major factors on the amount of water resources is river flow which is so dependent to the hydrologic and meteorologic phenomena. Simulation and forecasting of river flow makes the decision maker capable to effectively manage the water resources projects. So, simulation and forecasting ...
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Abstract
One of the major factors on the amount of water resources is river flow which is so dependent to the hydrologic and meteorologic phenomena. Simulation and forecasting of river flow makes the decision maker capable to effectively manage the water resources projects. So, simulation and forecasting models such as artificial neural networks (ANNs) are commonly used for simulation and predicting the exact value of such factors. In this research, the Dez River basin was selected as the case study. This paper investigates the effectiveness of temperature, precipitation and inflow factors and the lag time of those factors in inflow simulation and forecasting. Genetic algorithm (GA) has been thus used as an optimization tool, determining the optimum composition of the effective variables. Thus, in a flow simulation and forecasting model, the number of hidden layers, effective neurons in each layer, effective meteorologic and hydrologic parameters and also the lag time of each factor of flow simulation and forecasting has been considered as decision variables, and GA has been used to obtain the best combination of those variables. In this study, minimization of the total mean square error (MSE) has been considered as the objective function. Results show GA's effectiveness in flow simulation and forecasting with consistent accuracy. The value of R2 criterion has been obtained 0.86 and 0.79 in the simulation and forecasting models, respectively. The results also showed superiority replies obtained from the simulation model to the prediction model. One of the reasons for this superiority can be considering the meteorological factors in the current month in river flow simulation.
Keywords: Artificial Neural Network, Simulation, Forecasting, Flow, Optimization, Genetic Algorithm