Flood routing by Kidney algorithm and Muskingum model

Abstract

Flood is one of the natural hazards that its prediction and control is of great importance. One of the most important models in the field of flood routing is the Muskingum model. In this study, Muskingum four-parameter model is used for flood routing. The existence of unknown parameters causes the Kidney algorithm to be used as a new evolutionary algorithm based on reabsorption and filter operators for flood routing. The operators make the Kidney algorithm accelerate the convergence process and improve the quality of responses. Three floods were selected based on Kidney algorithm. The results indicated that the amount of sum squared deviation reduced by 84, 90, 35 and 86% for the Wilson flood algorithm compared to the Honey bee mating optimization, pattern search, particle swarm optimization (PSO) and harmony search (HS) methods for flood routing based on observational and simulated discharge. Also, the results indicated that the Kidney algorithm is more accurate based on the Muskingum four-parameter model than the Muskingum three-parameter model and the Muskingum two-parameter model. The sum absolute deviation value for the HS, genetic algorithm and PSO methods is 94, 88 and 82% higher than Kidney algorithm for Karahan flood. In addition, the predicted peak discharge for the Karahan flood and the predicted time for peak discharge were more accurate than other evolutionary algorithms. Also, the results of the Kidney algorithm for the Viessman and Lewis floods indicated that the Kidney algorithm well reduces the error indicators. Therefore, the Kidney algorithm as a suitable algorithm based on Muskingum four-parameter model had higher accuracy.