Inversion of the Permeability Coefficient of a High Core Wall Dam Based on a BP Neural Network and the Marine Predator Algorithm

Abstract

The parameters’ inversion of saturated–unsaturated is important in ensuring the safety of earth dams; many scholars have conducted some research regarding the inversion of hydraulic conductivity based on seepage pressure monitoring data. The van Genuchten model is widely used in saturated–unsaturated seepage analysis, which considers the permeability connected to the water content of the soil and the soil’s shape parameters. A BP neural artificial network is a mature prediction technique based on enough data, and the marine predator algorithm is a new nature-inspired metaheuristic inspired by the movement of animals in the ocean. The BP neural artificial network and marine predator algorithm are applied in the permeability coefficient inversion of a core-rock dam in China; the results show that in the normal operation status, the BP network shows better accuracy, and the average of the absolute error and variance of the absolute error are both minimum values, which are 2.21 m and 1.43 m, respectively. While the water storage speed changes, the marine predator algorithm shows better accuracy; the objective function is calculated to be 0.253. So, the marine predator algorithm is able to accurately reverse the desired results in some situations. According to the actual condition, employing suitable methods for the inverse permeability coefficient of a dam can effectively ensure the safe operation of dams.