Parameter adaptive Muskingum method of flood routing

The estimated parameter values of Muskingum model for the same river reach are typically different in different flood conditions,and statistical analysis also shows that these parameters are highly variable.As a result,if these parameters are specified as constants it is impossible to achieve a high accuracy of flood routing.This paper proposes a parameter adaptive Muskingum method for river flood routing.This method searches the flood sequences most similar to the current flood sequences by using the time-series similarity searching algorithm of data mining filed,then estimates the optimal parameter values of the Muskingum model based on the data of similar flood sequences.The set of parameters so obtained are the best for the current Muskingum flood routing.Aiming at the nonlinearity and complexity of Muskingum flood routing model,this paper puts forth a novel method for the parameter estimation with an immune clonal selection algorithm(ICSA).The simulation and application results show a faster convergence and higher accuracy of ICSA than other techniques such as ant colony algorithm and genetic algorithm,as well as a higher accuracy of the parameter adaptive Muskingum routing than traditional one.