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A technique for leakage reduction is pressure management, which considers the direct relationship between leakage and pressure. To control the hydraulic pressure in a water distribution system, water levels in the storage tanks should be maintained as much as the variations in the water demand allows. The problem is bounded by minimum and maximum allowable pressure at the demand nodes. In this study, a Genetic Algorithm (GA) based optimization model is used to develop the optimal hourly water level variations in a storage tank in different seasons in order to minimize the leakage level. Resiliency and failure indices of the system have been considered as constraints in the optimization model to achieve the minimum required performance. In the proposed model, the results of a water distribution simulation model are used to train an Artificial Neural Network (ANN) model. Outputs of the ANN model as a hydraulic pressure function is then linked to a GA based optimization model to simulate hydraulic pressure and leakage at each node of the water distribution network based on the water level in the storage tank, water consumption and elevation of each node. The proposed model is applied for pressure management of a major pressure zone with an integrated storage facility in the northwest part of Tehran Metropolitan area. The results show that network 438 S. Nazif et al.leakage can be reduced more than 30% during a year when tank water level is optimized by the proposed model.

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Water allocation in a competing environment is a major social and economic challenge especially in water stressed semi-arid regions. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. In this paper, two reservoir operation optimization models for water allocation to different users are presented. The objective functions of both models are based on the Nash Bargaining Theory which can incorporate the utility functions of the water users and the stakeholders as well as their relative authorities on the water allocation process. The first model is called GA-KNN (Genetic Algorithm-K Nearest Neighborhood) optimization model. In this model, in order to expedite the convergence process of GA, a KNN scheme for estimating initial solutions is used. Also KNN is utilized to develop the operating rules in each month based on the derived optimization results. The second model is called the Bayesian Stochastic GA (BSGA) optimization model. This model considers the joint probability distribution of inflow and its forecast to the reservoir. In this way, the intrinsic and forecast uncertainties of inflow to the reservoir are incorporated. In order to test the proposed models, they are applied to the Satarkhan reservoir system in the north-western part of Iran. The models have 2528 A. Ahmadi et al. unique features in incorporating uncertainties, facilitating the convergence process of GA, and handling finer state variable discretization and utilizing reliability based utility functions for water user sectors. They are compared with the alternative models. Comparisons show the significant value of the proposed models in reservoir operation and supplying the demands of different water users.

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Freshwater reservoirs are under threat because of huge amounts of sediments deposited annually. Sediment flushing seems to be effective to preserve reservoir storage, but it may have negative environmental impacts on downstream ecosystems such as fish mortality. Therefore, providing a suitable flushing strategy that could be compatible with the river ecosystem downstream is of great importance. Two numerical models were developed in this paper to predict the suspended sediment concentration (SSC) on the reservoir-river system and effects of different flushing scenarios on aquatic life. Developed models were applied to the Dez Resevoir system in the southwest of Iran which has suffered from the sediment problems in two last decades. The suitable values for flushing time, concentration limits, and flushing discharge have been recommended in this research by use of the existing information and previous flushing records, as well as field measurement and modeling. Based on social, environmental and technical limitations, March is the appropriate time for flushing. After hydraulic simulation of different flushing scenarios and sediment routing along the river, flushing with 1275 and 800 cubic meter per second with 30 and 20 g per lit concentration in dry and wet season respectively are feasible and have minimum environmental impacts.