Aquifer parameter inversion by artificial fish swarm algorithm based on quantum theory

Abstract

Hydrogeological parameters are important indicators for studying aquifer properties and constructing numerical models. Affected by various unknown underground factors or human factors, there’s still a big gap between the hydrogeological parameters of aquifers calculated by different traditional pumping test methods. In recent years, artificial intelligence algorithms have been successfully applied to aquifer parameter inversion, but for a single intelligence algorithm, each has its respective shortcomings. This paper combined the quantum computing theory with the Artificial Fish Swarm Algorithm (AFSA) to improve the performance of AFSA, and then proved the correctness and superiority of the proposed method via examples.