Abstract
Abstract In order to improve the estimation accuracy of stage–discharge relationship model, the back propagation neural network optimized through the genetic algorithm (GA-BP) based on information entropy was proposed. Firstly, the information entropy and hierarchical clustering were used to quickly cluster the hydrological sample data and get the optimal number of clusters. Secondly, the k-nearest neighbor algorithm was used to divide the new stage data into the most appropriate clustering categories. Finally, the river daily discharge was estimated. Some measured data collected from a hydrological station were used to test the model, and the simulation results showed that the method proposed by this paper can get higher estimation accuracy than the classical analytical model, BP neural network algorithm and GA-BP neural network algorithm, which provided a new effective method for parameter estimation of the stage–discharge relationship model.
Highlights
The stage–discharge relationship model is a curve describing the relationship between the water level of the basic section at the hydrological measuring station and the flow through the section
Birbal et al (2021) proposed a Gene Expression Programming (GEP) as an extension of Genetic Programming (GP) and applied it to stage–discharge curves. This method was compared with the traditional stage–discharge relationship curves (SRC) and regression methods, and the results show that the performance of GEP model is significantly better than that of GP model and traditional model
Most of the classical SRCs are based on empirical regression, which cannot be well applied to the study of flow characteristics of complex rivers
Summary
The stage–discharge relationship model is a curve describing the relationship between the water level of the basic section at the hydrological measuring station and the flow through the section. In order to improve the accuracy of stage–discharge curve, a back propagation neural network optimized through the genetic algorithm (GA-BP) based on information entropy is proposed in this paper This method is a non-parametric learning method based on machine learning. It does not have to form a clear hypothesis to define the complete objective function on the entire sample space, and it can form a different local approximation of the objective function for each query sample This method first uses information entropy and hierarchical clustering (HAC) to set up a non-analytical relationship model between stage and discharge samples, quickly clusters hydrological data samples and obtains the optimal number of clusters; uses K-nearest neighbor (KNN) method to classify the new water level data into the most appropriate cluster category; the daily flow of the river is estimated by using the newly established relationship model. When each new sample is classified, the river flow is estimated using the GA-BP algorithm in the previous section
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