Research on Forecasting Model of Daily Discharge in Karst Area Based on Mea Grey Neural Network. (June 2020)

• Record Type: Journal Article
• Authors: Li, Xia; Jin, Xin; Guo, Pan
• Is Part Of: Journal of physics. Volume 1549:Number 2(2020)
• Publication Date: 2020-06
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1549/2/022087
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The water-bearing system in the karst area is complex and changeable. Water in the water-bearing medium has the characteristics of coexistence of fissure flow and pipeline flow, coexistence of laminar and turbulent flow, coexistence of linear and nonlinear flow, coexistence of continuous flow and isolated water body. In areas where economic development is relatively backward, most areas lack data or no data. Based on the characteristics of the karst area in the southwest, this paper proposes a thinking evolution algorithm to optimize the gray neural network model. This method improves the optimization ability of runoff prediction models and effectively overcomes human neural network learning. The speed is slow and there are inherent shortcomings of local minima. After studying the daily flow forecast of Zhenlong Station, it is shown that the relative error of the prediction is small and can be effectively used for short-term runoff prediction.