Abstract:To improve the prediction accuracy of surface subsidence of the GNSS CORS automatic monitoring system in mining areas, we propose a combined prediction method based on the Kalman filter model optimized by genetic algorithm (GA-KF) and the BP neural network strong prediction model (BP-Adaboost) after phase space reconstruction based on the wavelet analysis. The trend sequence and random sequence of the original monitoring data are obtained by wavelet analysis, and are predicted by the GA-KF model and the BP-Adaboost model respectively. The sum of the two data is the final prediction result. Taking the data of Bozhou Banji monitoring station as an example, the experimental results show that: 1) Compared with the prediction values of using the single GA-KF and BP-Adaboost model after phase space reconstruction, the combined model has higher prediction accuracy. 2) It is found that the combined model is less affected by the length of modeling sequence, and the average relative error is within 0.003%, which is much smaller than the two single models and has a certain anti-interference ability.
ZHANG Can,Lü Weicai,GUO Zhongchen et al. An Optimized Combined Prediction Model for Surface Subsidence Based on GA-KF and BP-Adaboost[J]. jgg, 2023, 43(2): 203-208.