Metadata about: Scarcity of fresh water resources has thrown various challenges to hydrologist. Optimum usage of resource is the only way out to handle this situation. Among the various water resources the most controllable one is the dam reservoirs. This paper deals with optimal reservoir optimization using multi objective genetic algorithm (MOGA). Various parameters like reservoir storage capacity, spill loss, evaporation rate, water used for irrigation, water used for electricity production, rate of inflow, outflow all need to be managed in an optimal way so that water levels are managed and resource specifications are met. This is normally managed using a software, but sudden change in scenarios and change in requirements cannot be handled by such softwares. Hence we are incorporating an optimised software layer to handle such situation. Multi objective genetic algorithm was able to optimise the water usage within the usage constrains. The results were assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. The simulated result shows that MOGA derived rules are promising and competitive and can be effectively used for reservoir optimization operations.

About: Scarcity of fresh water resources has thrown various challenges to hydrologist. Optimum usage of resource is the only way out to handle this situation. Among the various water resources the most controllable one is the dam reservoirs. This paper deals with optimal reservoir optimization using multi objective genetic algorithm (MOGA). Various parameters like reservoir storage capacity, spill loss, evaporation rate, water used for irrigation, water used for electricity production, rate of inflow, outflow all need to be managed in an optimal way so that water levels are managed and resource specifications are met. This is normally managed using a software, but sudden change in scenarios and change in requirements cannot be handled by such softwares. Hence we are incorporating an optimised software layer to handle such situation. Multi objective genetic algorithm was able to optimise the water usage within the usage constrains. The results were assessed based on reliability, vulnerability and resilience indices. In addition, based on a multi-criteria decision-making model, it was evaluated by comparing it with other evolutionary algorithms. The simulated result shows that MOGA derived rules are promising and competitive and can be effectively used for reservoir optimization operations.

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