改进粒子群优化算法在滑坡监测数据融合中的应用OACSTPCD

Based on the exploration of multi-source data fusion technology and the application of the improved Genetic Algorithm(GA)-Particle Swarm Optimization(PSO)-BP Neural Network(GA-PSO-BP)model in the field of landslide monitoring and prediction,this study uses landslide monitoring data from Xiping Town,Anxi County,Fujian Province as an example to verify the effectiveness of integrating multiple data sources and optimizing BP neural networks with particle swarm algorithm.The results show that the GA-PSO-BP model can significantly improve the accuracy and reliability of landslide monitoring,effectively solve the problems of BP neural networks being prone to local optima and high demands for training data.The model's prediction of landslide displacement demonstrates lower Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)compared to traditional methods,indicating superior predictive performance.In processing multi-source data with high correlation and redundancy,the effectiveness of centralized and distributed data fusion methods offers new strategies and approaches for landslide early warning systems.