Enhancing Flood Routing Accuracy: A Fuzzified Approach to Nonlinear Variable-Parameter Muskingum Model

Variable-parameter Muskingum models emerge as a highly efficient approach among the prevalent hydrological flood routing methods, owing to their accuracy and robustness. This research introduces a novel partitioning framework aimed at refining outcomes from a nonlinear variable-parameter Muskingum model by introducing fuzzification to the adjacent sub-periods of the inflow hydrograph. The results prove the efficacy of the proposed method in enhancing the accuracy of routed outflow, aligning well with the inherent characteristics of a flooding event. Validation of the newly introduced fuzzified nonlinear variable-parameter Muskingum model was conducted using four distinct case studies from the literature, including Wilson's data, the flood events in Rivers Wye and Wyre, and Viessman and Lewis' data. The evaluation of the proposed framework's effectiveness utilized the Sum of Squared Deviations (SSQ) as the objective function of the model, along with six different supplemental metrics. The results demonstrated a meaningful increase in the accuracy of the nonlinear Muskingum model for the respective cases studied. The findings imply that the proposed partitioning framework is adaptable to the variable-parameter Muskingum models with an intensity-based partitioning technique, thereby advancing the results of this conventional flood routing method.