Neural Network-Based Monitoring Analysis and Optimization Design of Canal Bank Slope

In this paper, the proxy model of the functional function is constructed by using the BP neural network fitting method, and the inertia weight index of the traditional particle swarm algorithm is improved, and the slope safety factor solution method of the improved particle swarm algorithm combined with the BP neural network is formed, which is verified and solved through practical engineering cases, and the method of fitting the response surface of the functional function using the BP neural network is proposed. In this study, the LSTM deep learning network model is introduced, combined with the chaotic characteristic analysis of slope deformation. An effective slope deformation prediction method was successfully constructed. This method not only has high prediction accuracy and generalization ability, but also has important practical value and application prospects. In the future, we will continue to study the application of LSTM model in slope deformation prediction, in order to further improve the prediction accuracy and practicability. It can effectively solve the problem that the functional function cannot be explicitly expressed when solving the reliability of complex structures.