Multi-Objective Quantity–Quality Reservoir Operation in Sudden Pollution

• Published in: Water resources management 2014, Vol.28 (2), p.567-586
• Main Authors: Shokri, Ashkan, Bozorg Haddad, Omid, Mariño, Miguel A
• Format: Article
• Language: English
• Subjects: Agricultural engineering; Algorithms; Atmospheric Sciences; cities; Civil Engineering; Conflict resolution; Damage; disaster recovery; Dynamic programming; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Environment; Exact sciences and technology; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology; Hydrology. Hydrogeology; Hydrology/Water Resources; Investigations; issues and policy; Mathematical models; Methods; MTBE; Municipal water; Neural networks; Objective function; Optimization; Pollutants; Pollution; Pollution abatement; Pollution control; public water supply; Reservoir operation; Reservoirs; Salinity; Simulation; summer; Surface water; system optimization; Vaporization; VOCs; Volatile organic compounds; volatilization; Water allocation; Water pollution; Water quality; Water resources
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-013-0504-z

Damage caused by entered pollution in reservoirs can affect a water resource system in two ways: (1) Damages that are caused due to consumption of polluted water and (2) damages that are caused due to insufficient water allocation. Those damages conflict with each other. Thus, the crisis should be managed in a way that the least damage occurs in the water resource system. This paper investigates crisis management due to the sudden entrance of a 30 m³ methyl tert-butyl ether (MTBE) load to the Karaj dam in Iran, which supplies municipal water to the cities of Tehran and Karaj. To simulate MTBE advection, dispersion, and vaporization, the latter process is added to the CE-QUAL-W2 model. After that, the multi-objective NSGAII-ALANN algorithm, which is a combination of the NSGAII optimization method along with a multi layer perceptron (MLP), which is one of the most widely used artificial neural network (ANN) structures, is employed to extract the best set of decisions in which the two aforementioned damages are minimized. By assigning a specific importance to each objective function, after extracting the optimal solutions, it is possible to choose one of the solutions with the least damage. Four scenarios of entering pollution to the Karaj reservoir the first day of each season are considered, resulting in a Pareto set of operation policies for each scenario. Results of the proposed methodology indicate that if the pollution enters the reservoir in summer, by using one of the optimal policies extracted from the Pareto set of the 2nd Scenario, by a 36 % reduction in meeting the demand, allocated pollution decreases to about 60 %. In other seasons, there is a significant decrease in allocated pollution with a smaller reduction in the met demand.