三峡库区堆积层滑坡的变形趋势判断及预测
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摘要
为提高三峡库区堆积层滑坡变形趋势判断的准确性,该文以R/S分析和混沌优化PSO-SVM模型为基础,构建了滑坡变形趋势判断模型和变形预测模型,判断和预测三峡库区堆积层滑坡的变形趋势。经实例检验表明:R/S分析模型能很好地评价滑坡的变形趋势,且累计变形序列和速率变形序列的Hurst指数均大于0.5,呈持续增加趋势,但累计变形序列的Hurst指数相对更大,趋势性也相对更强;在预测过程中,混沌优化PSO-SVM模型的平均相对误差均小于2%,最大相对误差也仅为1.83%,具较高预测精度,满足期望要求,且变形预测结果与趋势判断结果具有较好的一致性;通过两实例的综合应用,验证了本文模型的普遍适用性。通过该文研究,相互佐证了趋势判断模型和变形预测模型在滑坡变形规律研究中的有效性,为滑坡变形规律研究提供了一种新的思路。
In order to improve the accuracy of judging the deformation trend of accumulative landslide in the Three Gorges Reservoir area, this paper constructs a landslide deformation trend judgment model and a deformation prediction model based on R/S analysis and chaotic optimization PSO-SVM model to judge and predict deformation trend of accumulated layer landslide in Three Gorges Reservoir area. Examples show that the R/S analysis model can evaluate the deformation trend of landslide very well, and the Hurst exponents of the cumulative deformation sequence and the rate deformation sequence are greater than 0.5, showing a continuous increasing trend, but the Hurst exponents of the cumulative deformation sequence are relatively larger and the trend is relatively stronger. In the prediction process, the average relative error of the chaotic optimization PSO-SVM model is less than 2%, and the maximum relative error is less than 2%. The difference is only 1.83%, which has a high prediction accuracy and meets the expectation requirements, and the deformation prediction results are in good agreement with the trend judgment results. Through the comprehensive application of two examples, the universal applicability of the model is verified. Through the research in this paper, the validity of trend judgment model and deformation prediction model in the study of landslide deformation law is proved, which provides a new way of thinking for the study of landslide deformation law.
In order to improve the accuracy of judging the deformation trend of accumulative landslide in the Three Gorges Reservoir area, this paper constructs a landslide deformation trend judgment model and a deformation prediction model based on R/S analysis and chaotic optimization PSO-SVM model to judge and predict deformation trend of accumulated layer landslide in Three Gorges Reservoir area. Examples show that the R/S analysis model can evaluate the deformation trend of landslide very well, and the Hurst exponents of the cumulative deformation sequence and the rate deformation sequence are greater than 0.5, showing a continuous increasing trend, but the Hurst exponents of the cumulative deformation sequence are relatively larger and the trend is relatively stronger. In the prediction process, the average relative error of the chaotic optimization PSO-SVM model is less than 2%, and the maximum relative error is less than 2%. The difference is only 1.83%, which has a high prediction accuracy and meets the expectation requirements, and the deformation prediction results are in good agreement with the trend judgment results. Through the comprehensive application of two examples, the universal applicability of the model is verified. Through the research in this paper, the validity of trend judgment model and deformation prediction model in the study of landslide deformation law is proved, which provides a new way of thinking for the study of landslide deformation law.
引文
[1] 张国栋,龙海涛,易庆林,等.基于地表位移监测成果的水库型滑坡变形机制分析[J].水利学报,2014,45(S2):73- 76.
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[3] 刘广宁,黄波林,王世昌.三峡库区巴东李家湾滑坡变形破坏机理研究[J].人民长江,2016,47(8):39- 42.
[4] 代贞伟,殷跃平,魏云杰,等.三峡库区藕塘滑坡变形失稳机制研究[J].工程地质学报,2016,24(1):44- 55.
[5] 明成涛,肖诗荣,宋桂林,等.三峡库区曾家棚滑坡变形破坏影响因素分析[J].人民长江,2013,44(24):30- 34.
[6] 江巍,陈玮,孙冠华,等.基于DDA方法的藕塘滑坡失稳模式分析[J].防灾减灾工程学报,2016,36(4):551- 558.
[7] 刘艺梁,殷坤龙,汪洋,等.基于经验模态分解和神经网络的滑坡变形预测研究[J].安全与环境工程,2013,20(4):14- 17.
[8] 王鸣,易武.小波-差分指数平滑模型在滑坡变形预测中的应用[J].人民长江,2015,46(12):45- 47.
[9] 江洎洧,潘家军,张伟.地震作用下三峡库区典型堆积层滑坡变形特性研究[J].地震工程学报,2015,37(s2):142- 146.
[10] 肖志勇,邓华锋,李建林,等.库水位间歇性下降对堆积体滑坡稳定性的影响[J].长江科学院院报,2016,33(8):114- 119.
[11] 余吉安,杨斌,陈哲.基于修正R/S模型族的中国上市家族企业股票风险特征研究[J].北京交通大学学报(社会科学版),2012,11(4):30- 37.
[12] 张文远,王祯意,孟楠丁.基准风格资产的收益率长记忆性研究——基于修正的R/S模型[J].经济问题,2014(2):59- 63.
[13] 赵艳南,牛瑞卿,彭令,等.基于粗糙集和粒子群优化支持向量机的滑坡变形预测[J].中南大学学报(自然科学版),2015,46(6):2324- 2332.
[14] 王彬.堆积层滑坡位移R/S分形参数演化特征与失稳判据研究[D].青岛:青岛理工大学,2012.
[2] 陈洪凯,周晓涵,唐红梅,等.水库岸坡地质灾害研究现状与趋势[J].重庆师范大学学报(自然科学版),2016,33(3):38- 44.
[3] 刘广宁,黄波林,王世昌.三峡库区巴东李家湾滑坡变形破坏机理研究[J].人民长江,2016,47(8):39- 42.
[4] 代贞伟,殷跃平,魏云杰,等.三峡库区藕塘滑坡变形失稳机制研究[J].工程地质学报,2016,24(1):44- 55.
[5] 明成涛,肖诗荣,宋桂林,等.三峡库区曾家棚滑坡变形破坏影响因素分析[J].人民长江,2013,44(24):30- 34.
[6] 江巍,陈玮,孙冠华,等.基于DDA方法的藕塘滑坡失稳模式分析[J].防灾减灾工程学报,2016,36(4):551- 558.
[7] 刘艺梁,殷坤龙,汪洋,等.基于经验模态分解和神经网络的滑坡变形预测研究[J].安全与环境工程,2013,20(4):14- 17.
[8] 王鸣,易武.小波-差分指数平滑模型在滑坡变形预测中的应用[J].人民长江,2015,46(12):45- 47.
[9] 江洎洧,潘家军,张伟.地震作用下三峡库区典型堆积层滑坡变形特性研究[J].地震工程学报,2015,37(s2):142- 146.
[10] 肖志勇,邓华锋,李建林,等.库水位间歇性下降对堆积体滑坡稳定性的影响[J].长江科学院院报,2016,33(8):114- 119.
[11] 余吉安,杨斌,陈哲.基于修正R/S模型族的中国上市家族企业股票风险特征研究[J].北京交通大学学报(社会科学版),2012,11(4):30- 37.
[12] 张文远,王祯意,孟楠丁.基准风格资产的收益率长记忆性研究——基于修正的R/S模型[J].经济问题,2014(2):59- 63.
[13] 赵艳南,牛瑞卿,彭令,等.基于粗糙集和粒子群优化支持向量机的滑坡变形预测[J].中南大学学报(自然科学版),2015,46(6):2324- 2332.
[14] 王彬.堆积层滑坡位移R/S分形参数演化特征与失稳判据研究[D].青岛:青岛理工大学,2012.
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