Research on stage–discharge relationship model based on information entropy

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 A GA-BP algorithm based on information entropy is proposed.; Information entropy and hierarchical clustering were used to obtain the optimal number of clustering.; Through the model comparison, the GA-BP model based on information entropy has high accuracy.; This paper presented a new method for flow estimation.;

Keywords

• ga-bp
• hierarchical clustering
• information entropy
• k-nearest neighbor
• neural network
• stage–discharge relationship