Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration its parameters remains an active challenge researchers. task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account parameter uncertainty. In this work, model proposed where assessment outflow as quantity based on linear with inputs. This calculation can be achieved extension principle sets and logic. critical point calibration method. Due complexity model, particle swarm optimization (PSO) enable use simulation process each possible solution that composes swarm. A weighted sum several performance criteria fitness function PSO. accounts inclusive constraints (the property data must included within produced band) magnitude band, since large uncertainty may render non-functional. Four case studies from references are benchmark method, including smooth, double, non-smooth complex, real study shows advantages approach. closer uncertain nature problem. new methodology increases reliability prediction. Furthermore, band include, significant degree, observed output existent methodologies even if they include more complex assumptions.