Development of A Novel Discharge Routing Method Based On the Large Discharge Dataset, Muskingum Model, Optimization Methods, and Multi-Criteria Decision Making

Discharge routing is a key method for predicting downstream hydrograph in canals or rivers. Predicting the downstream hydrograph using the upstream hydrograph can significantly decrease the flood damages. A combination of a large dataset and Muskingum model can enhance river discharge routing reliability. However, calibrating the Muskingum model for large datasets requires powerful optimization algorithms. Metaheuristic Optimization Algorithms (MOAs) can accurately calibrate the Muskingum model, but their performance may vary. Hence, in the present study, a new technique is introduced for discharge routing based on the large discharge dataset: Muskingum model, MOAs, and Multi-criteria Decision-Making (MCDM). Different MOAs, including a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Shark Smell Optimization (SSO), Whale Optimization Algorithm (WOA), Harris Hawk''s Optimization (HHO), and hybrid of WOA and CS (WOA_CS), were developed for Muskingum calibration. The Mollasani-Ahvaz, Harmaleh-Bamdej river reaches Karun basin, and Lighvan-Heravi in the Urmia basin are considered case studies. Results of discharge routing based on the evaluation criteria in the training period showed MOAs were trained with high accuracy and reliability. While in the testing period, each MOA achieves better results in some evaluation criteria. Considering all evaluation criteria using the MCDM showed that WOA_CS, WOA, and FFA were placed in the first, second, and third rankings, respectively. The MCDM scores for WOA_CS, WOA, and FFA were equal to 0.960, 0.913, and 0.907, respectively. The developed method in this study has a good potential for discharge routing in various river reaches.