In this research, a deterministic algorithm based on the Genetic Optimization (GA) method was used for optimal water allocation to different uses from surface and underground resources in the operation of Yamchi reservoir dam in Ardabil province. The objective function in the optimized model was to maximize the profit of water allocated to the agricultural sector according to the selected cropping pattern, and the supply of drinking and environmental requirements were considered as a constraint in the optimal model. The rainfall and inflow to the reservoir are discretized due to their non-deterministic property and then for different values of the initial volume of the reservoir at the beginning of the operation period and any logical combination of rainfall and inflow to the reservoir (under the three scenario: minimum, normal and maximum) the optimal model was implemented. The results showed that in the minimal and normal scenario with increasing the initial volume of the reservoir, the profit was increased, but in maximum scenario, the initial volume of the reservoir had no effect on the profit. The smaller the initial volume of the reservoir the lower the relative yield of the products, which was most evident in the minimum scenario. Also, the amount of water allocated from groundwater resources to agricultural sector varies from 3. 1 to 7. 9 MCM, which corresponds to the potential for extracting water through utilization wells.

Despite the development of technical tools for the analysis of complex systems, the most important issue in resolving water resource problems focuses on the interaction of human and natural systems. Agent based modeling (ABM) has been used in recent years as an effective tool for the development of integrated human and environmental models. One of the main challenges of ABM application in water resources management is to identify and characterize key agents. In this study, an ABM has been developed for optimal water allocation from dam reservoir to downstream needs. The proposed model includes reservoir simulation using Standard Operating Policy, optimization of water allocation using Genetic Algorithm, and behavioral simulation of agents using ABM. The behavioral simulation model simulated stakeholders interactions and their reactions to water allocation decisions. To develop this model, key agents including drinking, industry, agriculture, environment and policy sector were identified in the Tajan basin. The proposed model is a new method for simulation of stakeholder interactions and establishing a hydrological-environmental-human relationship to manage demand and to optimize water allocation to water needs. The obtained results showed that the proposed model has a high potential for increasing agent utility and total profit through agents feedback analysis, so that agricultural agent utility increased from 19% to 47% and the total profit increased up to 21. 4%.

Dam construction on the rivers causes sediment accumulation behind them. Determination of quantity and quality of sediment deposition in reservoirs is important for their stability and operation. Area- reduction method is one of the empirical methods for reservoir sediment distribution. In this method, reservoir is divided to four types, based on its shape. Parameters are presented for each types of reservoir which is carried out based on sediment distribution. Determination of suitable parameters in operating reservoirs which have at least one period of hydrographic data, leads to increment of accuracy. The objective of this research is to determine the optimal parameters of empirical area reduction method in Karaj dam using Shuffled Complex Evolution algorithm. In this research, a computer model was developed based on area reduction method theory. Then optimization model was prepared using Shuffled Complex Evolution algorithm and these two models were combined and Simulation-Optimization model was developed. Simulation- Optimization model determined area reduction method parameters for Karaj dam so that the most compatibility occurs between computational and measured volumes. To determine optimal parameters values, objective function was defined as Root Mean Square Error (RMSE) calculation of actual values. Using prepared model, optimal parameters were obtained based on information in 1961 and hydrography in 1991. To verify simulation-optimization model, sediment distribution in 2007 was calculated using obtained optimal parameters by model and was compared to hydrographic values. Results showed that values of objective function were reduced up to 62% and 48% in calibration and verification periods, respectively. Then, sediment distributions of Karaj dam were predicted based on optimal parameters.

Background and Objectives: Nowadays, water scarcity is a major challenge for our country, Iran. Therefore, storage and optimal operation of limited resources, including water stored in dams' reservoirs, is one of the issues of interest for researchers in the field of water resources. In this paper, optimization of single-reservoir operation problem is solved by using one of the newest heuristic algorithms, named Charged System Search algorithm. Generally, this algorithm is based on the electrostatics laws to determine the quantity of resultant force. Kaveh and Talatahari (2010a) proposed this algorithm for the first time and examined its capabilities for solving engineering problems and sample functions. Results showed that the algorithm has good performance. Therefore, its use for solving engineering optimization problems is recommended. However, a review of literature shows that using of this algorithm is very limited in the field of water resources engineering. Materials and Methods: In this paper, the simple and hydropower operation of Dez Reservoir, over 5 and 20 years of operation period are solved using the proposed algorithm. In order to solve these problems, two different formulations are proposed considering water release or storage volume as decision variables of the problem in the first and second formulations, respectively and the results are compared to other available methods. Results: Comparison of the results shows the capability of the proposed algorithm, in which the results of first formulation are better than the second one’ s. In other words, the results of first formulation for solving simple operation problem over 5 and 20 years are reduced by 11. 29% and 16. 69% in comparison with the results of second formulation and also the results are improved by 20. 06% and 37. 66% using the first formulation for solving hydropower problem. Furthermore, the results of the proposed algorithm for solving simple operation problem over 5 and 20 years are reduced by 33. 64% and 74. 97% in comparison with the results of Particle Swarm Optimization algorithm and also the results of using the proposed algorithm for solving hydropower problem are improved by 6. 53% and 41. 48%. In addition, the results of the proposed algorithm for solving simple operation problems over 5 and 20 years are reduced by 7. 79% and 35. 59% in comparison with the results of Genetic Algorithm and also the results of using proposed algorithm for solving hydropower problem are improved by 11. 32% and 67. 43%. Conclusion: Investigating these results with the results obtained by using other existing algorithms indicates a better performance of the Charged System Search algorithm for solving the reservoir operation optimization problem. According to these results, the use of this algorithm is recommended for solving other problems in the field of water engineering.

Given the high potential for flooding in many regions of Iran, optimal reservoir management for reducing flood-related damages is of significant importance. This study aims to provide an optimization model for managing reservoir operations during flood events. In this research, the Karun Dam and the 2019 flood were selected as the case study. A Particle Swarm Optimization (PSO) algorithm was used to minimize the difference between the release discharge and the permissible discharge (damage threshold downstream). The decision variables were the daily release values from the reservoir during the flood period. The results showed that using the optimization model significantly improved the objective function (the difference from permissible discharge). For permissible discharges of 400, 600, and 800 cubic meters per second, the improvement in the objective function was 11%, 17%, and 37%, respectively. Additionally, the maximum release discharge decreased from about 2,500 cubic meters per second in the actual operation to around 1,900 cubic meters per second in the optimized model. Sensitivity analysis indicated that increasing the maximum permissible storage volume in the reservoir could further reduce damages. This study demonstrates that optimal reservoir operation can significantly reduce flood-related damages. Furthermore, reducing reservoir storage before peak rainfall events and gradual water release are among the proposed strategies. The findings of this study can assist in improving reservoir operation policies.

Water resources developments and the future planning need the management tools which would be capable to simulate the complicated systems in order to support decisions making. The main objective of this method of simulation is its simplicity and rapid learning of system behavior in present and future. System dynamics is a feedback –based object- oriented simulation approach which represents complex dynamic systems in a realistic way and also allows the involvement of users in model development. By this way, the confidence in modeling process increases. In this study, a dynamic model of Damghan dam (located in Semnan province) has been developed. The aim of this study is to determine the effect of different operation policies on the behavior of reservoir in supplying downstream demands until 2030. For this purpose, eight scenarios proposed. Among them, three scenarios give the best results in supplying the downstream demands. It is revealed that the application of suitable policies can provide the present demands and also the future developments. In this study, the dam height is optimized considering the suitable supply for downstream demands.

Watershed management practices (WMPs) include methods to create, enhance, and maintain vegetation to reduce surface runoff in the watershed. The implementation of WMPs at the watershed scale is an ongoing challenge that needs to take into account tradeoffs between hydrological and economic objectives using optimization algorithm. This research demonstrate a methodology and decision support tool that suggests the optimal location of biological watershed management practices (including improvement rangeland cover) which minimize the cost and simultaneously reduce surface runoff to target levels cases (i. e. %20, %15, %10 and, %25) at watershed scale. The decision support tool (SWATGA) consists of a semi-distributed hydrologic model, Soil and Water Assessment Tool (SWAT) which simulation of runoff in watershed, a genetic algorithm (GA), which serves as the optimization engine for the location of biological watershed management practices across the rangeland of the watershed. The proposed method was then applied to Taleghan watershed with an approximate area of 900 km2 that located in Alborz Mountain, Iran. Results indicate watershed management practices should use in %48, %35, %22 and %62 of total area of watershed to reduction %20, %15, %10 and %25, respectively. The maximum decrease and increase runoff after the implementation of WMPs have been occurring in the spring and summer, respectively. Using this decision support tool, for each level of target goals, decision makers can select the optimal combination of watershed management practices.

Occurring periods of drought in the past decade, increasing growth of population, and limiting surface water resources necessitate the proper management of the reservoirs of dams. Operation of reservoirs is influenced by a lot of goals which many of them are incompatible with each other. The inflows to the reservoir and the storage volumes are uncertain which increase complexity in the operation of the reservoir. The main challenge is to find the best situation of the reservoir release and optimize hydro systems. Various optimization methods have been introduced for the operation of the reservoir. But some of these methods have disadvantages that use of them is not possible in all conditions. Bozorg Haddad (2005) used Honey Bees Mating optimization for solving design problems and the Ant Colony algorithm was also used to exploit a four-reservoir system in a discrete space that was able to optimize the problem with greater accuracy and less computing time than the genetic algorithm (Jalali et al. 2007). Mousavi et al. (2017) used the Harmony Search Algorithm for the optimization of water powerhouse storage projects and reported satisfactory results. Harmony Search algorithm was presented by Geem et al. for the first time in 2000. In this research Harmony Search Algorithm (HSA) is evaluated to determine the optimal operation of multi-reservoir systems. Then in order to evaluate the ability of the algorithm to solve real problems, the optimal operation of Dez Dam reservoir in Khuzestan province, Iran, has been considered for the period of 10 years (1990-1992) with 120 months. In the single-reservoir of Dez Dam, the goal is to provide of agricultural demand of downstream or to determine the optimal monthly release for 10 years operation. The optimum value was obtained by using a linear programming model (Lingo). Lingo model can solve nonlinear problems and provides the global optimum in some cases such as the intended problem where the objective function is convex. Therefore, the solutions obtained from the HSA model were compared with the outcomes of Lingo software. A new heuristic algorithm derived from an artificial phenomenon found in musical performance, namely the process of searching for the better harmony, has been introduced. Music harmony is a combination of sounds considered pleasing from an aesthetic point of view. Harmony in nature is a special relationship between several sound waves that have different frequencies. Musical performances seek the best state (fantastic harmony) determined by aesthetic estimation, as the optimization algorithms seek the best state (global optimum-minimum cost or maximum benefit or efficiency) determined by objective function evaluation. Aesthetic estimation can be found by the set of the sounds played by joined instruments, just as objective function evaluation which is obtained by the set of the values produced by component variables. The sounds for better aesthetic estimation can be improved through several practices; just as the values for better objective function, evaluation can be improved by several iterations. The new algorithm is named Harmony Search (HS) and the steps in the procedure of HS are as follows: Step 1) Initialize a Harmony Memory (HM); Step 2) Improvise a new harmony from HM; Step 3) If the new harmony is better than the least harmony in HM, include the new harmony in HM, and exclude the minimum harmony from HM; Step 4) If stopping criteria are not satisfied, go to Step 2. Harmony Memory Considering Rate (HMCR ranges from 0 to 1. If a uniformly generated value between 0-1 occurs above the current value of the HMCR, then HS finds notes randomly within the possible playable range without considering HM. For improving solutions and escaping local optima, another option may be introduced. This option mimics the pitch adjustment of each instrument for tuning the ensemble. For computation, the pitch adjustment mechanism is devised as shifting to neighboring values within a range of possible values. A Pitch Adjusting Rate (PAR) of 0. 10 means that the algorithm chooses a neighboring value with 10% probability (an upper value with 5% or lower value with 5%. In the present study, first HSA was used for the optimization of a four-reservoir system. The objective function was calculated as308. 2915 and 308. 2900 using Lingo software and HSA, respectively, which had a different of 0. 0005 percent with the global optimum. After the success of HSA in solving the four-reservoir system, a ten-reservoir system was considered. This time, the objective function was calculated as1194. 4 and 1193. 1 by using Lingo software and HSA, respectively, which had a different of 0. 1 percent with the global optimum. In the single-reservoir problem of Dez Dam, the value of global optimum of the objective function was calculated as 1. 9188 and 1. 944 by using Lingo and HSA, respectively, which had the different of 1. 31% with the global optimum. So, it can be concluded that this algorithm has the ability to solve optimization problems of the real system.

Mathematical simulations on dam break or failure using Boss Dambrk hydrodynamic flood routing dam break model were carried out to determine the extent of flooding downstream, flood travel times, flood water velocities, and impacts on downstream affected residences, properties and environmental sensitive areas due to floodwaters released by failure of the dam structure. Computer simulations for one of the worse-case scenarios on dam failure using BOSS DAMBRK software accounted for dam failure, storage effects, floodplains, over bank flow and flood wave attenuation. The simulated results reviewed a maximum flow velocity of 2.40 m/s with a discharge (Q) of approximately 242 m³/s occurred at 1.00 km downstream. The maximum discharge increased from 244 m3/s (flow velocity = 1.74 m/s occurred at 8th km) to 263 m3/s (flow velocity = 1.37 m/s occurred at 12th km); about a 39% drop in flow velocity over a distance of 4.00 km downstream. If the entire dam gives way instantly, some spots stretching from 0.00 km (at dam site) to approximately 3.40 km downstream of the dam may be categorized as “danger zone”, while downstream hazard and economic loss beyond 3.40 km downstream can be classified as “low” or “minimal” zones.

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