基于决策树的滑坡预报判据数据挖掘研究
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摘要
自2003年三峡水库开始蓄水后,在库水位周期波动的作用下,三峡库区的地质环境更加恶化,致使大量古滑坡复活,如白水河滑坡等。在降雨和库水的诱发因素影响下,这些滑坡的变形往往表现出周期波动性增加。这种滑坡在失稳前要经历多次加速变形,采用传统的工程防治方法耗财耗力。为了避免灾害发生或减轻灾害影响,事先对滑坡做出预报,判断其变形情况,并采取有效的防范措施显得尤为重要。
滑坡预报问题的核心是预报模型的建立与预报判据的挖掘。自20世纪60年代开始,国内外专家经过数十年的悉心钻研,在滑坡预测预报模型和理论方面取得了长足进展,提出了多种不同参数的滑坡预报判据,并且在实践上也积累了成功预报的经验。但是由于滑坡变形演化过程的随机性、复杂性和不确定性,现有的滑坡预测预报模型和预报判据仍存在着一些明显的缺点或不足,并未真正揭示滑坡变形的本质和综合考虑各种影响因素。
面对海量的滑坡数据资料,本文将数据挖掘方法和技术引入到研究中,以白水河滑坡为例,综合分析滑坡变形的时空演化特征,发现白水河滑坡目前并未进入加速变形阶段。受时间尺度的影响很难对白水河滑坡的破坏发生时间做出准确预报,故本文利用数据挖掘方法对其进行了中长期定性与定量趋势预报,挖掘出综合预报判据并提出综合预报指数的概念。最后利用综合预报指数建立时间序列ARIMA位移预测模型,取得了良好的预测结果。通过对这些问题的分析与研究,论文取得了以下成果：
1)分析了白水河滑坡变形的时空演化特征。
通过对滑坡的累计位移-时间曲线分析,发现白水河滑坡的变形演化过程具有明显的阶梯形特征,且滑坡中前部变形较明显,后缘变形缓慢,判断该滑坡属于牵引式滑坡。通过研究该滑坡的裂缝分布,发现该滑坡目前没有形成圈闭的裂缝贯通体系,整体滑动边界并未形成,综合判断该滑坡尚未进入加速变形阶段。
2)建立了白水河滑坡降雨和库水诱发因素中长期预报判据。
首先利用K-Means算法对滑坡变形的月位移量进行聚类,根据滑坡变形演化特征,将各期滑坡变形阶段划分成三类：缓慢变形阶段、累进变形阶段和快速变形阶段；然后利用决策树C5.0算法对降雨(日降雨量、月降雨量)、库水(升降、平均库水位变化率)诱发因素构建滑坡变形趋势预报模型,归纳出诱发因素预报判据。
3)结合宏观变形等因子建立滑坡综合预报判据,并提出综合预报指数(I)概念。
利用决策树C5.0算法,结合挖掘出的诱发因素判据、宏观变形特征(裂缝、局部变形等)、深部位移变化率构建综合预报模型,预报结果的Kappa系数均达到了0.9以上。分析归纳出的综合预报判据,发现各判据多是各因子共同叠加影响滑坡变形,且过于精确绝对,不能全面表现出各因子叠加组合。为此,提出了综合预报指数(I)的概念,利用挖掘出的综合预报判据中的最优分割点将各个因子划分成四类,并对各个类别赋予相同的权重,利用构建决策树模型时计算的各个因子变量的重要性程度作为因子权重,然后将各因子权重叠加,得到综合预报指数,确定该值的范围是1.0-4.0。
4)利用时间序列方法对滑坡累计位移量进行了定量预测。
论文对2004年1月到2010年11月的位移监测数据进行分析,按照月划分时间区间,得到83期位移数据,对前60期数据利用ARIMA算法构建累计位移-综合预报指数模型,对后23期数据进行预测验证,平均绝对误差(MAE)为25.014,证明利用综合预报指数(I)构建位移预测模型史能全而反映滑坡变形受自身、诱发因素、外界环境变化等因素的影响,对滑坡位移量的预测准确度较高。
Since 2003, the Three Gorges Reservoir began impoundment, which made the geology environment worsen based on the cycle action of water level, and induced a lot of ancient landslide resurrection, such as Baishuihe landslide, etc. Based on the influence of the factors of rainfall and reservoir water, these landslides deformation often increased of cycle volatility. Because this landslide before instability often experiences many accelerating deformation, using traditional engineering prevention ways will exhaust wealth and resources. In order to avoid disaster or ease of the influences of landslide disaster, it is particularly important to forecast, judge its deformation and take effective prevention measures.
Prediction model and the prediction criterion is core of the landslide problem. Since the 20th century 60's, after several decades of domestic and foreign experts carefully study, the landslide prediction model and the theory has made considerable progress, made a variety of different parameters of the landslide prediction criterion, and also accumulated the experience of successful prediction in practice. However, due to the randomness, complexity and uncertainty of the evolution of slope deformation, the existing landslide prediction model and criterion still have some obvious shortcomings or deficiencies, did not really reveal the nature of landslide deformation and overall consideration of various kinds of factors.
Facing massive landslide data, this paper introducing data mining methods and technology into the study, comprehensive analysis the spatial and temporal evolution of Baishuihe landslide deformation, finds that the landslide is not currently in the accelerating deformation stage. It is difficult to forecast accurately the destruction time of Baishuihe landslide because of the time scale. So this paper uses data mining technology to carry out long-term trends in qualitative and quantitative forecast, digs out comprehensive forecast criterion and makes the concept of comprehensive prediction index. Through analysis and study of these issues, the paper has achieved the following results:
(1) The spatial and temporal evolution of Baishuihe landslide deformation is analysed.
Through the analysis of landslide cumulative displacement-time curve, found that there is obvious characteristics of the ladder-shaped of the evolution of Baishuihe landslide deformation, and the the deformation in front of the landslide is obvious, while slow at the trailing edge, which proved the landslide is towed to determine. Studying the cracks distribution of the landslide, found that the landslide currently does not formate cracks traps system, the overall slip boundary does not form, and makes a comprehensive judgment of the landslide which is not yet accelerated deformation stage.
(2) Middle and long-term forecast criterion of predisposing factors of Baishuihe landslide is established such as rainfall and reservoir.
Firstly, using K-Means algorithm to cluster the displacement of months, according to the evolution of landslide deformation, the deformation of each phase of the landslide is divided into three categories, namely the slow deformation stage, progressive deformation stage and rapid deformation stage; And then using C5.0 decision tree algorithm to construct landslide trend forecasting model about predisposing factors, such as rainfall (daily rainfall, monthly rainfall), reservoir water (going up and down, the average water level change rate), and summarize prediction criterion of predisposing factors.
(3) Combined Macroscopic deformation and other factors to establish a comprehensive prediction criterion of landslide, and proposed the concept of comprehensive prediction index.
Using predisposing factor criterion, macroscopic deformation (cracks, local deformation, etc.), deep displacement change rate by C5.0 decision tree algorithm to build an integrated forecasting model, Kappa coefficient of forecasting results reached 0.9. Analyzing the comprehensive prediction criterion, found that the criterion is the more common superimposed factors to affect the landslide deformation, and too precise and absolute to fully show the factors stacking combinations. So this paper proposed the concept of comprehensive prediction index (Ⅰ). Each factor is divided into four categories by the optimal cut point criteria digged out, and each category is assigned the same weight. Then this paper gets the comprehensive prediction index, through stacking each factor weight, which is the importance degree of each factor calculated at the time of constructing decision tree model. And the value range is 1.0-4.0.
(4) Used time series methods to quantitatively predict cumulative displacement of landslide.
Analysing Displacement monitoring data in January 2004 to November 2010, displacement data of 83 segments was gained in accordance with the time interval by month. Using the former 60 segments data to construct the model of accumulated displacement data-comprehensive prediction index with ARIMA algorithm, and to predict the latter 23 segments data.The average absolute error (MAE) is 25.014mm, which proved that using the comprehensive prediction index (Ⅰ) to construct the prediction model of displacement can fully reflect the landslide deformation by itself, predisposing factors, changes in the external environment and other factors and the landslide displacement amount of prediction accuracy is higher.
滑坡预报问题的核心是预报模型的建立与预报判据的挖掘。自20世纪60年代开始,国内外专家经过数十年的悉心钻研,在滑坡预测预报模型和理论方面取得了长足进展,提出了多种不同参数的滑坡预报判据,并且在实践上也积累了成功预报的经验。但是由于滑坡变形演化过程的随机性、复杂性和不确定性,现有的滑坡预测预报模型和预报判据仍存在着一些明显的缺点或不足,并未真正揭示滑坡变形的本质和综合考虑各种影响因素。
面对海量的滑坡数据资料,本文将数据挖掘方法和技术引入到研究中,以白水河滑坡为例,综合分析滑坡变形的时空演化特征,发现白水河滑坡目前并未进入加速变形阶段。受时间尺度的影响很难对白水河滑坡的破坏发生时间做出准确预报,故本文利用数据挖掘方法对其进行了中长期定性与定量趋势预报,挖掘出综合预报判据并提出综合预报指数的概念。最后利用综合预报指数建立时间序列ARIMA位移预测模型,取得了良好的预测结果。通过对这些问题的分析与研究,论文取得了以下成果：
1)分析了白水河滑坡变形的时空演化特征。
通过对滑坡的累计位移-时间曲线分析,发现白水河滑坡的变形演化过程具有明显的阶梯形特征,且滑坡中前部变形较明显,后缘变形缓慢,判断该滑坡属于牵引式滑坡。通过研究该滑坡的裂缝分布,发现该滑坡目前没有形成圈闭的裂缝贯通体系,整体滑动边界并未形成,综合判断该滑坡尚未进入加速变形阶段。
2)建立了白水河滑坡降雨和库水诱发因素中长期预报判据。
首先利用K-Means算法对滑坡变形的月位移量进行聚类,根据滑坡变形演化特征,将各期滑坡变形阶段划分成三类：缓慢变形阶段、累进变形阶段和快速变形阶段；然后利用决策树C5.0算法对降雨(日降雨量、月降雨量)、库水(升降、平均库水位变化率)诱发因素构建滑坡变形趋势预报模型,归纳出诱发因素预报判据。
3)结合宏观变形等因子建立滑坡综合预报判据,并提出综合预报指数(I)概念。
利用决策树C5.0算法,结合挖掘出的诱发因素判据、宏观变形特征(裂缝、局部变形等)、深部位移变化率构建综合预报模型,预报结果的Kappa系数均达到了0.9以上。分析归纳出的综合预报判据,发现各判据多是各因子共同叠加影响滑坡变形,且过于精确绝对,不能全面表现出各因子叠加组合。为此,提出了综合预报指数(I)的概念,利用挖掘出的综合预报判据中的最优分割点将各个因子划分成四类,并对各个类别赋予相同的权重,利用构建决策树模型时计算的各个因子变量的重要性程度作为因子权重,然后将各因子权重叠加,得到综合预报指数,确定该值的范围是1.0-4.0。
4)利用时间序列方法对滑坡累计位移量进行了定量预测。
论文对2004年1月到2010年11月的位移监测数据进行分析,按照月划分时间区间,得到83期位移数据,对前60期数据利用ARIMA算法构建累计位移-综合预报指数模型,对后23期数据进行预测验证,平均绝对误差(MAE)为25.014,证明利用综合预报指数(I)构建位移预测模型史能全而反映滑坡变形受自身、诱发因素、外界环境变化等因素的影响,对滑坡位移量的预测准确度较高。
Since 2003, the Three Gorges Reservoir began impoundment, which made the geology environment worsen based on the cycle action of water level, and induced a lot of ancient landslide resurrection, such as Baishuihe landslide, etc. Based on the influence of the factors of rainfall and reservoir water, these landslides deformation often increased of cycle volatility. Because this landslide before instability often experiences many accelerating deformation, using traditional engineering prevention ways will exhaust wealth and resources. In order to avoid disaster or ease of the influences of landslide disaster, it is particularly important to forecast, judge its deformation and take effective prevention measures.
Prediction model and the prediction criterion is core of the landslide problem. Since the 20th century 60's, after several decades of domestic and foreign experts carefully study, the landslide prediction model and the theory has made considerable progress, made a variety of different parameters of the landslide prediction criterion, and also accumulated the experience of successful prediction in practice. However, due to the randomness, complexity and uncertainty of the evolution of slope deformation, the existing landslide prediction model and criterion still have some obvious shortcomings or deficiencies, did not really reveal the nature of landslide deformation and overall consideration of various kinds of factors.
Facing massive landslide data, this paper introducing data mining methods and technology into the study, comprehensive analysis the spatial and temporal evolution of Baishuihe landslide deformation, finds that the landslide is not currently in the accelerating deformation stage. It is difficult to forecast accurately the destruction time of Baishuihe landslide because of the time scale. So this paper uses data mining technology to carry out long-term trends in qualitative and quantitative forecast, digs out comprehensive forecast criterion and makes the concept of comprehensive prediction index. Through analysis and study of these issues, the paper has achieved the following results:
(1) The spatial and temporal evolution of Baishuihe landslide deformation is analysed.
Through the analysis of landslide cumulative displacement-time curve, found that there is obvious characteristics of the ladder-shaped of the evolution of Baishuihe landslide deformation, and the the deformation in front of the landslide is obvious, while slow at the trailing edge, which proved the landslide is towed to determine. Studying the cracks distribution of the landslide, found that the landslide currently does not formate cracks traps system, the overall slip boundary does not form, and makes a comprehensive judgment of the landslide which is not yet accelerated deformation stage.
(2) Middle and long-term forecast criterion of predisposing factors of Baishuihe landslide is established such as rainfall and reservoir.
Firstly, using K-Means algorithm to cluster the displacement of months, according to the evolution of landslide deformation, the deformation of each phase of the landslide is divided into three categories, namely the slow deformation stage, progressive deformation stage and rapid deformation stage; And then using C5.0 decision tree algorithm to construct landslide trend forecasting model about predisposing factors, such as rainfall (daily rainfall, monthly rainfall), reservoir water (going up and down, the average water level change rate), and summarize prediction criterion of predisposing factors.
(3) Combined Macroscopic deformation and other factors to establish a comprehensive prediction criterion of landslide, and proposed the concept of comprehensive prediction index.
Using predisposing factor criterion, macroscopic deformation (cracks, local deformation, etc.), deep displacement change rate by C5.0 decision tree algorithm to build an integrated forecasting model, Kappa coefficient of forecasting results reached 0.9. Analyzing the comprehensive prediction criterion, found that the criterion is the more common superimposed factors to affect the landslide deformation, and too precise and absolute to fully show the factors stacking combinations. So this paper proposed the concept of comprehensive prediction index (Ⅰ). Each factor is divided into four categories by the optimal cut point criteria digged out, and each category is assigned the same weight. Then this paper gets the comprehensive prediction index, through stacking each factor weight, which is the importance degree of each factor calculated at the time of constructing decision tree model. And the value range is 1.0-4.0.
(4) Used time series methods to quantitatively predict cumulative displacement of landslide.
Analysing Displacement monitoring data in January 2004 to November 2010, displacement data of 83 segments was gained in accordance with the time interval by month. Using the former 60 segments data to construct the model of accumulated displacement data-comprehensive prediction index with ARIMA algorithm, and to predict the latter 23 segments data.The average absolute error (MAE) is 25.014mm, which proved that using the comprehensive prediction index (Ⅰ) to construct the prediction model of displacement can fully reflect the landslide deformation by itself, predisposing factors, changes in the external environment and other factors and the landslide displacement amount of prediction accuracy is higher.
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