Abstract
This paper discusses the Muskingum model as a novel parameter estimation method. Sixty representative floods over the past four decades serve as research objects; a linear Muskingum model and Pigeon-inspired optimization (PIO) algorithm are used to obtain the parameters of each flood. The proposed “in-process type” dynamic parameter estimation (IP-DPE) method is used to establish the characteristic attributes set of 50 floods. The characteristic attributes set refers to a set of parameters that could describe the shape, magnitude, and duration of the flood before flood peak; they are the input, whereas parameters K and x of each flood are the output to establish a Neural Network model. Then we input flood characteristic attributes to obtain flood parameters when estimating flood parameters practically. Ten floods were used to test the parameter estimation and flood routing efficacy. The results show that the IP-DPE method can quickly identify parameters and facilitate accurate river flood forecasting.
Highlights
The Muskingum model has been commonly utilized for river flood forecasting across various parts of the world since it was first proposed in the 1930s [1,2]
In researching the more commonly used Muskingum model parameter estimation methods, we observed a tendency to set parameters according to mean values or flow magnitude, in which the practicability of the model and the applicability of the parameter are not fully considered
We assert that because each flood has its own particular parameters, that an “in-process type” dynamic parameter estimation method based on the Pigeon-inspired optimization (PIO) algorithm and BP-Artificial Neural Network model is the better choice for building a highly accurate Muskingum model
Summary
The Muskingum model has been commonly utilized for river flood forecasting across various parts of the world since it was first proposed in the 1930s [1,2]. For the model to work properly, its parameters must be accurately estimated Several methods for such parameter estimation have been proposed in recent years, including trial-and-error [3], the least-square method [4,5,6], and nonlinear programming [7,8]. These approaches come with high computational complexity, poor universality, and susceptibility to local optima [9]. The Muskingum model is an effective channel flood routing model that has been widely used.
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