| 基于最优分解模态和GRU模型的库岸滑坡位移预测研究 |
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| 引用本文： | 罗袆沅,蒋亚楠,许强,唐斌.基于最优分解模态和GRU模型的库岸滑坡位移预测研究[J].武汉大学学报(信息科学版),2023,48(5):702-709. |
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| 作者姓名： | 罗袆沅 蒋亚楠 许强 唐斌 |
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| 作者单位： | 1.成都理工大学地质灾害防治与地质环境保护国家重点实验室, 四川 成都, 610059 |
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| 基金项目： | 国家重点研发计划2017YFC1501004四川省科技厅重点研发项目2020YFS0353四川省科技厅重点研发项目2020YFS0353四川省教育厅重点研发项目18ZA0054国家重点实验室开放基金SKLGP2017K016 |
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| 摘 要： | 在滑坡位移综合预测研究中，常因滑坡随机位移分量无法准确提取、最优训练数据集及时效性无法确定等，造成多源监测数据利用不充分、位移预测结果不稳定。鉴于此，引入变分模态分解，在滑坡位移时序分析的基础上，结合门控循环单元递归神经网络，提出一种新型滑坡位移综合预测模型。以三峡库区白水河滑坡为例，选取2003-07—2012-12的位移监测数据和同时期库水位及降雨数据进行分析研究，综合模型预测结果的均方根误差为9.715 mm，判定系数为0.967。对比实验分析表明，该模型在保证高预测精度的同时，在有效预测时长和时效性上同样优势明显，在库岸滑坡位移预测研究中具有很强的应用和推广价值。
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| 关 键 词： | 滑坡位移预测 灰狼优化 变分模态分解 门控循环单元 |
| 收稿时间： | 2021-10-26 |
Displacement Prediction of Reservoir Bank Landslide Based on Optimal Decomposition Mode and GRU Model |
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| Affiliation: | 1.State Key Laboratory of Geological Hazard Prevention and Geological Environment Protection, Chengdu University of Technology, Chengdu 610059, China2.School of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China |
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| Abstract: | Objectives The inadequate utilization of multisource monitoring data and the unstable results of displacement prediction are often caused by inaccurate extraction of random components, uncertain optimal training data set and timeliness in the comprehensive landslide displacement prediction study. Methods On that account, a new landslide prediction model is proposed by integrating the variational mode decomposition with the gated recurrent unit recurrent neural network on the basis of landslide displacement time series analysis. Results Taking Baishuihe landslide in the Three Gorges Reservoir Area as an example, the monitoring data including displacement and reservoir water level and rainfall data from July 2003 to December 2012 are selected for analysis and research. The root mean square error of the predicted value of the model is 9.715 mm and the coefficient of determination is 0.967. The results show that the model guarantees high prediction accuracy and has obvious advantages in effectiveness and timeliness as well. Conclusion Therefore, it has a strong application and popularization value in reservoir bank landslide displacement prediction research. |
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