A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction

This paper proposes a WD-GA-LSSVM model for predicting the displacement of a deepseated landslide triggered by seasonal rainfall,in which wavelet denoising(WD)is used in displacement time series of landslide to eliminate the GPS observation noise in the original data,and genetic algorithm(GA)is applied to obtain optimal parameters of least squares support vector machines(LSSVM)model.The model is first trained and then evaluated by using data from a gentle dipping(~2°-5°)landslide triggered by seasonal rainfall in the southwest of China.Performance comparisons of WD-GA-LSSVM model with Back Propagation Neural Network(BPNN)model and LSSVM are presented,individually.The results indicate that the adoption of WD-GA-LSSVM model significantly improves the robustness and accuracy of the displacement prediction and it provides a powerful technique for predicting the displacement of a rainfall-triggered landslide.     

滑坡监测预报方法研究现状

滑坡是一种十分严重的自然灾害,会给人类造成重大的损失,已引起世界各国的广泛关注;针对滑坡灾害的各项研究正在不断地开展、深入;对目前滑坡监测和预报方法的研究现状进行总结并作了探讨.     

Progressive modelling of the gravity-induced landslide using the local dynamic strength reduction method

The failure of slope is a progressive process, and the whole sliding surface is caused by the gradual softening of soil strength of the potential sliding surface. From this viewpoint, a local dynamic strength reduction method is proposed to capture the progressive failure of slope. This method can calculate the warning deformation of landslide in this study. Only strength parameters of the yielded zone of landslide will be reduced by using the method. Through continuous local reduction of the strength parameters of the yielded zone, the potential sliding surface developed gradually and evolved to breakthrough finally. The result shows that the proposed method can simulate the progressive failure of slope truly. The yielded zone and deformation of landslide obtained by the method are smaller than those of overall strength reduction method. The warning deformation of landslide can be obtained by using the local dynamic strength reduction method which is based on the softening characteristics of the sliding surface.     

新铺下二台滑坡变形机制及中长期预报模型

以奉节新铺下二台滑坡为例,基于GPS位移监测数据、裂缝数据、降雨量及库水位等多源数据,总结分析了大型古滑坡的复活规律,引入滑坡中长期预报模型,实现了以季度或月份为时间单位的跨水文年滑坡位移预测,并通过岩土体蠕变压缩模型,验证了推移式滑坡后缘裂缝形成机理。结果表明：(1)降雨是下二台滑坡变形的主导因素,滑坡变形使得滑体产生裂缝并成为降雨入渗通道,加剧了岩体破碎与软弱层软化,降低了滑坡稳定性,集中持续降雨可使滑坡失稳破坏;(2)通过模型预测值与地表监测数据的比较,将年降雨量作为滑坡中长期预报模型中的主控因素具有实际可操作性且有助于提高滑坡中长预报精度;(3)推移式滑坡后缘裂缝由滑坡推移式位移和岩土体压缩形成,引入蠕变压缩模型计算的裂缝宽度并和监测数据的比较说明,蠕变压缩模型非常适合该类边坡,同时应用岩土体蠕变压缩模型反推得到岩土体平均变形模量,判断岩体破碎程度,可以为滑坡稳定性分析及后续工程治理提供参考。     

基于SSA-Adam-BP神经网络模型的堰塞坝稳定性预测

现有的堰塞坝稳定性预测模型多为线性模型,无法充分考虑堰塞坝稳定性与其形态特征和水域条件之间的复杂非线性关系。鉴于此,结合反向传播神经网络模型和樽海鞘优化算法,提出了一种新型的堰塞坝稳定性预测模型SSA-Adam-BP。该模型通过网格搜索法选取确定模型结构的最佳超参数组合,进而利用交叉验证和绘制ROC曲线的方式分别对采用不同优化算法的模型进行评估。使用开源数据库中的全球153例堰塞坝数据对模型的实际应用进行了说明及验证。与传统线性模型的对比表明神经网络模型预测准确率较高,具有较低的误报率。将SSA与Adam优化算法结合提高了BP模型的全局搜索能力,其平均交叉验证准确率达到了91.73%,能够使用较少的参数实现对堰塞坝稳定性快速准确的预测。SSA-Adam-BP模型对近年来典型工程的稳定性能够准确预测,具有一定的实用性和系统平台推广应用价值。     

Spatial prediction of landslide for Badong County in the ThreeGorges reservoir district

By using the landslide risk evaluating model and the advantages of GIS technology in image processing and space analysis, the relative landslide hazard and risk evaluating system of the new county site of Badong is built up. The system is mainly consisted of four subsystems： Information management subsystem, hazard as- sessment subsystem, vulnerability evaluation subsystem and risk prediction subsystem. In the system, landslide hazard assessment, vulnerability evaluation, risk predictions are carried out automatically based on irregular units. At last the landslide hazard and risk map of the study area is compiled. During the whole procedure, Matter-Element Model, Artificial Neural Network, ancl Information Model are used as assessment models. This system provides an effective way for the landslide hazard information management and risk prediction of each district in the Reservoir of Three Gorge Project. The result of the assessment can be a gist and ensure for the land planning and the emigration project in Badong.     

Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir

In order to reach the designated final water level of 175 m, there were three impoundment stages in the Three Gorges Reservoir, with water levels of 135 m, 156 m and 175 m. Baishuihe landslide in the Reservoir was chosen to analyze its displacement characteristics and displacement variability at the different stages. Based on monitoring data, the landslide displacement was mainly influenced by rainfall and drawdown of the reservoir water level. However, the magnitude of the rise and drawdown of the water level after the reservoir water level reached 175 m did not accelerate landslide displacement. The prediction of landslide displacement for active landslides is very important for landslide risk management. The time series of cumulative displacement was divided into a trend term and a periodic term using the Hodrick-Prescott(HP) filter method. The polynomial model was used to predict the trend term. The extreme learning machine(ELM) and least squares support vector machine(LS-SVM) were chosen to predict theperiodic term. In the prediction model for the periodic term, input variables based on the effects of rainfall and reservoir water level in landslide displacement were selected using grey relational analysis. Based on the results, the prediction precision of ELM is better than that of LS-SVM for predicting landslide displacement. The method for predicting landslide displacement could be applied by relevant authorities in making landslide emergency plans in the future.     

基于双重注意力机制的滑坡识别方法优化

随着计算机视觉技术的发展,通过卫星图像深度学习进行滑坡识别的研究正在逐步展开。通过引入双重注意力机制,提出了一种基于卷积神经网络的滑坡图像识别优化算法。基于统计的2 200张滑坡图像数据集,探讨了10种网络结构及4种注意力机制对滑坡识别结果的影响,并通过比例为4∶1的训练集和测试集进行滑坡识别,验证了本文方法的有效性。结果表明：ResNet结构相较于其他网络结构表现更为优秀,就该算例而言,ResNet-101结构具有最高的召回率、精确率和F1度量。融入了双重注意力机制的卷积神经网络相较于单个神经网络而言,滑坡识别的精确率更大,且滑坡边界分割结果更接近于真实的滑坡边界,其中,ResNet-101+DAN模型为最优模型。相较之下,单个神经网络无法克服图像噪声的影响,图像分割结果不佳。     

Spatial prediction of landslide for Badong County in the Three Gorges reservoir district

By using the landslide risk evaluating model and the advantages of GIS technology in image processing and space analysis, the relative landslide hazard and risk evaluating system of the new county site of Badong is built up. The system is mainly consisted of four subsystems: Information management subsystem, hazard assessment subsystem, vulnerability evaluation subsystem and risk prediction subsystem. In the system, landslide hazard assessment, vulnerability evaluation, risk predictions are carried out automatically based on irregular units. At last the landslide hazard and risk map of the study area is compiled. During the whole procedure, Matter-Element Model, Artificial Neural Network, and Information Model are used as assessment models. This system provides an effective way for the landslide hazard information management and risk prediction of each district in the Reservoir of Three Gorge Project. The result of the assessment can be a gist and ensure for the land planning and the emigration project in Badong.     

Rapid prediction models for 3D geometry of landslide dam considering the damming process

The geometry of a landslide dam plays a critical role in its stability and failure mode, and is influenced by the damming process. However, there is a lack of understanding of the factors that affect the 3D geometry of a landslide dam. To address this gap,we conducted a study using the smoothed particle hydrodynamics numerical method to investigate the evolution of landslide dams. Our study included 17 numerical simulations to examine the effects of several factors on the geometry of landslide d...     

Application of topography fast marching method in landslide

Geophysical exploration methods are important tools for landslide disaster assessment, landslide treatment scheme design, and landslide prevention engineering. Seismic exploration, as an important geophysical exploration method, plays an critical role in geological disaster evaluation. Traveltime is one of the most frequently used seismic attributes. Among many different traveltime calculation methods, the fast marching method(FMM) is featured for its advantages in high efficiency, high accuracy and strong stability. In this paper, the velocity models are established according to the real landslide models, and then the topography FMM is applied to these landslide models. The calculation results show that topography FMM outperforms in calculating the traveltime for landslides.     

折射法和高密度电法在古滑坡勘探中的应用

古滑坡体地球物理调查方法技术的选择对勘探效果至关重要。以吉林省通化市拟建集安-通化高速公路滑坡体为例,采用折射波法和高密度电法,利用二维反演技术进行数据处理。结合地质信息,确定了滑坡体的形态及厚度变化,厚度最大约20 m,滑坡性质为堆积层滑坡。研究结果表明折射波法和高密度电法进行古滑坡勘探,效果较好。     

Numerical modeling and dynamic analysis of the 2017 Xinmo landslide in Maoxian County,China

A catastrophic landslide occurred at Xinmo village in Maoxian County, Sichuan Province,China, on June 24, 2017. A 2.87×106 m3 rock mass collapsed and entrained the surface soil layer along the landslide path. Eighty-three people were killed or went missing and more than 103 houses were destroyed. In this paper, the geological conditions of the landslide are analyzed via field investigation and high-resolution imagery. The dynamic process and runout characteristics of the landslide are numerically analyzed using a depth-integrated continuum method and Mac Cormack-TVD finite difference algorithm.Computational results show that the evaluated area of the danger zone matchs well with the results of field investigation. It is worth noting that soil sprayed by the high-speed blast needs to be taken into account for such kind of large high-locality landslide. The maximum velocity is about 55 m/s, which is consistent with most cases. In addition, the potential danger zone of an unstable block is evaluated. The potential risk area evaluated by the efficient depthintegrated continuum method could play a significant role in disaster prevention and secondary hazard avoidance during rescue operations.     

GIS-based spatial prediction of landslide using road factors and random forest for Sichuan-Tibet Highway

Accurate evaluation of landslide susceptibility is very important to ensure the safe operation of mountain highways.The Sichuan-Tibet Highway,which traverses th...     

Preliminary studies on the role of protozoa in the food web of Donghu Lake

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Preliminary studies on methane fluxes in Hainan mangrove communities

INTRODUCTIONChrbondioxideandInehanearethetwomostabundantabosphericcarbonspedes.Methane'sconcenhati0nintheboposphereinonsesO.7-l.l%peryearraasmussenandKhaili,l98l,BlakeandRowand,l988,Scheeetal.,l989).Ihauseofmethne'sforpaCtontheearth'sclirnateandthechernistryoftheatInosphere,thebudgetofabosphericmethanehasmivedconsiderableattchti0n.Wetlandsareestirnataltobeoneofthelamptsoimofabosphericmethane,aocountingforab0ut4O%to5O%0ftheglobalInehanesoonannually(Cforneand0reInland,l988,WhitingandC…     

考虑线状环境因子适宜性和不同机器学习模型的滑坡易发性预测建模规律

对于滑坡易发性预测中的水系、公路和断层等线状环境因子, 现有研究大多采用缓冲分析提取距离线状因子的距离。但缓冲分析得到的线距离属于离散型变量, 带有大小不等的随机波动性且对点或线要素的误差较为敏感, 导致滑坡易发性建模精度下降。提出了使用水系和公路的空间密度等连续型变量改进线状环境因子的适宜性。以江西省安远县为例, 选取高程、地形起伏度、距水系和公路距离等14个环境因子(原始因子), 再将距水系和公路距离2个线状因子改进为水系密度和公路密度(改进因子); 之后采用逻辑回归、多层感知器、支持向量机和C5.0决策树等机器学习模型, 分别构建了基于原始因子和改进因子的机器学习模型以预测滑坡易发性; 最后利用ROC曲线和易发性指数分布特征等来研究建模规律。结果表明: ①改进因子机器学习预测精度均高于原始因子机器学习模型, 表明空间密度对于易发性预测的适宜性更好; ②在4类机器学习模型中C5.0模型对于滑坡易发性预测性能最好, 其次是SVM、MLP和LR; ③水系和公路两类环境因子的重要性较高且使用改进因子机器学习后这两类环境因子重要性排名依然非常靠前。      

Dynamic prediction of landslide displacement using singular spectrum analysis and stack long short-term memory network

An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides.     

Mapping of geomorphic dynamic parameters for analysis of landslide hazards: A case of Yangbi river basin on the upper Lancang-Mekong of China

Landslides are common hazards in orogenic belt areas. However, it is difficult to quantitatively express the driving effects of tectonic uplift and stream erosion on the occurrence of landslides on large spatial scales by conducting field investigations. In this study, we analyzed a relatively large region that extends over the Yangbi River basin on the upper Lancang-Mekong in China. A series of quantitative indices, including kernel density of the landslide(KDL), hypsometric integral(HI), steepness index(ksn), stream power(?), and stream power gradient(ω) were used to explore the promoting effects of tectonic uplift and stream action intensity on landslides by mapping geomorphic dynamic parameters combined with actual landslide data. The analysis showed that the HI value in the highest landslide risk area was approximately 0.47, and that the KDL in the region can be expressed as a function of steepness or stream power gradient of the channel network, namely, KDL = 0.0127 Ln ksn-0.0167(R~2 = 0.72, P 0.001) and KDL = 0.0219 Ln ω-0.0558(R~2 = 0.21, P 0.02). Therefore, the lower reach of the Yangbi River basin, with higher steepness and stream power gradient, usually has a high uplifting rate and stream incision that drives landslides and causes the entire river network system to be in a stage of longterm active erosion. Furthermore, the results suggest that sediments were being rapidly discharged from the steep tributary channels to the mainstream. This practical situation highlights that the downstream area of the river basin is a high-risk area for landslide hazards, especially in association with heavy rainfall and earthquakes.     

西安市"城中村"问题初探

“城中村”是中国城市化进程中的一个特殊现象，随着西安城市总体规划进程的加快及区域经济的激烈竞争，尤其是二环内的众多村庄，逐渐被新崛起的高楼大厦包围。这些城中村“脏、乱、差”的环境、落后的管理体制、复杂的人口结构和危险的建筑群落等因素给西安城市建设和管理带来困难。随着西部大开发和西安大发展的不断深入，为把西安建设成西部经济强市，认识并改造“城中村”已经成为西安迫切面对的问题。改造应着重从利益兼顾、体制转变、法制健全和思想观念等方面人手，以国务院批准的西安第三次城市总体规划为指导，最终实现该市城乡结合的繁荣新局面。以该市后村、祭台村为例，阐述了西安“城中村”的现状及引发的问题，并提出村民利益要实惠、可见，健全法律、法规制度，产权转变要快和做好“城中村”改造的双向宣传工作等改造建议。