Abstract
Groundwater table often shows complex nonlinear characteristic. Back Propagation (BP) neural network is increasingly used to predict groundwater table. But man-made selecting the structure of BP neural network has blindness and expends much time. In order to overcome shortcomings of traditional BP neural network, Particle Swarm Optimization (PSO) algorithm was adopted to automatically search BP neural network weight matrix and threshold matrix. At the same time, linear inertia weight and chaos variation operator are presented to improve traditional PSO algorithm searching capacity. Study case shows that, compared with groundwater level prediction model based on BP neural network, the new prediction model based on PSO and BP neural network can greatly improve the convergence speed and precision.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.