三峡库区共有滑坡1 000余处,频繁发生的滑坡灾害极大威胁着人民生命财产安全,因此开展合理有效的滑坡位移预测对减少财产损失和拯救人民的生命具有重要的研究意义。以三峡库区白家包滑坡为例,针对当前滑坡位移预测中常用分解方法的局限,在位移时间序列的分解中引入可以控制分解模态数目的变分模态分解方法,选取不同模态参数进行对比,以提高分解模型的精度和有效性;并基于滑坡触发因子建立深度置信网络模型对位移子序列进行预测,重构所有子序列预测结果得到总的位移预测值。总位移预测均值绝对误差3.657 mm,平均绝对百分比误差为0.010%,总体预测精度高,该方法误差小,具有良好的应用指导意义。
Abstract
Frequent landslide disasters in the Three Gorges Reservoir area threaten the safety of people’s lives and property. Predicting landslide displacement rationally and effectively is of crucial significance for reducing property losses and protecting people’s lives. In view of the limits of conventional decomposition methods, the variational decomposition method which could control the number of decomposition modes was introduced into the decomposition of displacement time series. The Baijiabao landslide in the Three Gorges reservoir area was taken as a case study. The parameters are compared to improve the accuracy and effectiveness of the decomposition model. Moreover, a deep confidence network model involving landslide triggers was established to predict the displacement subsequences, and the results of all subsequences are reconstructed to obtain the total displacement prediction value. The mean average error of predicted total displacement is 3.657 mm, and the mean average percentage error is 0.010%, indicating a high accuracy.
关键词
滑坡 /
变分模态分解 /
深度置信神经网络 /
位移预测 /
误差分析
Key words
landslide /
variational mode decomposition /
deep confidence neural networks /
displacement prediction /
error analysis
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 田宏岭.降雨滑坡预警平台系统研究[D].成都:中国科学院研究生院(成都山地灾害与环境研究所),2007.
[2] 许 强,汤明高,徐开祥, 等. 滑坡时空演化规律及预警预报研究[J]. 岩石力学与工程学报, 2008, 27(6):1104-1104.
[3] 彭 令,牛瑞卿,吴 婷. 时间序列分析与支持向量机的滑坡位移预测[J]. 浙江大学学报(工学版), 2013, 47(9):1672-1679.
[4] 邓冬梅,梁 烨,王亮清,等. 基于集合经验模态分解与支持向量机回归的位移预测方法:以三峡库区滑坡为例[J]. 岩土力学, 2017, 38(12):3660-3669.
[5] 张凯翔,牛瑞卿,胡友健,等. 基于小波变换及外因响应的滑坡位移预测[J]. 中国矿业大学学报, 2017, 46(4):924-931.
[6] 任 超,梁月吉,庞光锋, 等. 经验模态分解和遗传小波神经网络法用于边坡变形预测[J]. 测绘科学技术学报, 2014,31(6):551-555.
[7] DRAGOMIRETSKIY K, ZOSSO D. Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3):531-544.
[8] 柴瑞敏,佀称称.一种改进的深度置信网络及其在自然图像分类中的应用[J].计算机应用与软件,2016,33(9):221-223.
[9] HINTON G E. Deep Belief Networks[J]. Scholarpedia, 2009, 4(6):5947.
[10]刘方园,王水花,张煜东.深度置信网络模型及应用研究综述[J].计算机工程与应用,2018,54(1):11-18.
[11]REN F , WU X , ZHANG K , et al. Application of Wavelet Analysis and a Particle Swarm-optimized Support Vector Machine to Predict the Displacement of the Shuping Landslide in the Three Gorges, China[J]. Environmental Earth Sciences, 2015, 73(8):4791-4804.
[12]徐 峰,范春菊,徐勋建,等. 基于变分模态分解和AMPSO-SVM耦合模型的滑坡位移预测[J]. 上海交通大学学报, 2018, 52(10): 1388-1395.
基金
中国地质调查局地质调查项目(0431203);三峡后续工作地质灾害防治与研究项目(0001212018CC60010,0001212012AC50021)
PDF(7716 KB)
