Resumen
This study develops and evaluates a simulation-optimization approach to mitigate the environmental impacts of freshwater pulses in brackish-water lakes whilst maximizing flood diversion benefits. Lake Pontchartrain, located downstream of the Mississippi River, Mississippi, United States, is a brackish-water ecosystem threatened by reducing salinity concentrations due to freshwater pulses from the flood diversion project on the Mississippi River. An adaptive neuro-fuzzy-inference-system-based model was developed as a data-driven model for simulating salinity distribution at a representative station of Lake Pontchartrain. Then, the data-driven model was used as the simulator in the optimization system. Both single-objective and multi-objective particle swarm optimizations were used to find the optimal solutions. Results show that the data-driven model is robust at simulating the salinity time series in the brackish-water ecosystem of Lake Pontchartrain. The Nash?Sutcliffe efficiency index of the data-driven model between measured and modelled salinity is 0.85, which means the model is reliable for applying in further simulations. The proposed optimal solutions for the environmental management of the lake indicate that because of the magnitude of the volume of freshwater released, environmental impacts at this location cannot be optimized through varying the timing and volume of the releases. This work presents a novel contribution to science through developing an optimization framework for mitigating the impacts of flood management on changes in salinity in brackish-water systems.