基于信息分解条件的滑坡变形预测

目的] 有效掌握滑坡变形规律，实现对滑坡变形的高精度预测。方法] 基于滑坡现场变形监测成果，先利用优化经验模态实现其变形数据的信息分解，再利用优化径向基神经网络和马尔科夫链完成滑坡变形的分项组合预测；最后，利用季节性Kendall检验判断滑坡变形趋势，以验证预测结果的可靠性。结果] 经验模态能有效分解滑坡变形信息，且通过优化处理，能进一步提高分解效果，并以互补式集合经验模态的分解效果最优；同时，预测结果的平均相对误差均小于2%，具有较高的预测精度，验证了预测模型的有效性，且变形趋势判断结果与预测结果较为一致，说明预测过程较为可靠，两者均得出滑坡变形呈持续增加趋势。结论] 由于滑坡变形具增加趋势，其稳定性趋于不利方向发展，应尽快开展滑坡灾害防治。

Landslide Deformation Prediction Based on Information Decomposition

Objective] The law of landslide deformation was effectively studied in order to produce high-precision predictions of landslide deformation. Methods] Using the results of landslide deformation monitoring, an optimized empirical model was used to decompose deformation data. The optimized radial basis function neural network and Markov chain were then used to complete the sub-item combination prediction of landslide deformation. Finally, the seasonal Kendall test was used to judge the landslide deformation trend to verify the reliability of the prediction results. Results] The empirical model effectively decomposed landslide deformation information, and the decomposition effect was further improved through optimization. The decomposition effect of the complementary ensemble empirical model was the best. The average relative error of the prediction results was less than 2%. This high prediction accuracy verified the effectiveness of the prediction model. The deformation trend judgment results were consistent with the prediction results, indicating that the prediction process was reliable, and that landslide deformation was increasing continuously. Conclusion] Because landslide deformation tends to increase over time and landslide stability tends to develop in an unfavorable direction, landslide disaster prevention and control should be carried out as soon as possible.