National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

China is one of the countries where landslides caused the most fatalities in the last decades.The threat that landslide disasters pose to people might even be greater in the future,due to climate change and the increasing urbanization of mountainous areas.A reliable national-scale rainfall induced landslide suscep-tibility model is therefore of great relevance in order to identify regions more and less prone to landslid-ing as well as to develop suitable risk mitigating strategies.However,relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area.The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China.In this context,it is aimed to explore the benefit of mixed effects mod-elling to counterbalance associated bias propagations.Six influencing factors including lithology,slope,soil moisture index,mean annual precipitation,land use and geological environment regions were selected based on an initial exploratory data analysis.Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information:Set 1(disregards the presence of incomplete landslide information),Set 2(excludes factors related to the incompleteness of landslide data),Set 3(accounts for factors related to the incompleteness via random effects).The vari-able sets were then introduced in a generalized additive model(GAM:Set 1 and Set 2)and a generalized additive mixed effect model(GAMM:Set 3)to establish three national-scale statistical landslide suscep-tibility models:models 1,2 and 3.The models were evaluated using the area under the receiver operating characteristics curve(AUROC)given by spatially explicit and non-spatial cross-validation.The spatial pre-diction pattern produced by the models were also investigated.The results show that the landslide inven-tory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models.The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However,although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9),it was not associated with the most plausible representation of landslide susceptibility.The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias.The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility.However,a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g.,the Kuenlun Mountains in the northern Tibetan Plateau).The non-linear mixed-effects model(Model 3)reduced the impact of these biases best by introducing bias-describing variables as random effects.Among the three models,Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive perfor-mance(median AUROC of spatial cross validation 0.84)compared to the other two models(median AUROCs of 0.81 and 0.79,respectively).We conclude that ignoring landslide inventory-based incomplete-ness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.     

Multivariate models for landslide hazard evaluation

As part of a long-term project aimed at a better understanding of the geological and geomorphological factors that control slope instability phenomena in Southern Italy, multivariate models for assessing landslide incidence hazard were developed and tested in two Calabrian sample areas characterized by different geological-geomorphological conditions. Discriminant analysis, based on a large set of mappable geological and geomorphical variables, is able to discriminate rather successfully between stable and unstable areas or slope units. Multiple regression analysis has also proved to be a useful tool in predicting actual and potential landslide hazard. Consequently, geomathematical models may provide a feasible approach to environmental hazard assessment, particularly when applied within the framework of a wider multidisciplinary project for land evaluation and planning.     

Local rainfall thresholds for forecasting landslide occurrence: Taipingshan landslide triggered by Typhoon Saola

This paper presents a case study of the Taipingshan landslide, which was triggered by Typhoon Saola in 2012. Taipingshan villa is one of the most famous scenic locations within the Taipingshan National Forest Recreation Area in northern Taiwan. Since the early 1990s, evidence of recent landslide activity appeared throughout the Taipingshan villa and included features such as tension cracks, ground settlement, and cracking in manmade structures. In response, a series of geological investigations and in-site/laboratory tests were conducted in 2010 to estimate slope stability and predict critical rainfall thresholds (event accumulated rainfall) for landslide activity. Results revealed that the critical rainfall threshold for the Taipingshan National Forest Recreation Area is 1765 mm. In 2012, that threshold was tested when Typhoon Saola brought tremendous rainfall to northern and eastern Taiwan and triggered activity along the main scarp of sliding mass B located near the History Exhibition Hall. According to in situ extensometer readings and on-site precipitation data, the extensometer was severed at an accumulated rainfall 1694 mm. Field monitoring data during the typhoon event are in good agreement with the rainfall threshold. These preliminary results suggest that the threshold may be useful for assessing the rainfall threshold of other landslides and a good reference for establishing early warning systems for landslides.     

Implementing landslide path dependency in landslide susceptibility modelling

Landslide susceptibility modelling—a crucial step towards the assessment of landslide hazard and risk—has hitherto not included the local, transient effects of previous landslides on susceptibility. In this contribution, we implement such transient effects, which we term “landslide path dependency”, for the first time. Two landslide path dependency variables are used to characterise transient effects: a variable reflecting how likely it is that an earlier landslide will have a follow-up landslide and a variable reflecting the decay of transient effects over time. These two landslide path dependency variables are considered in addition to a large set of conditioning attributes conventionally used in landslide susceptibility. Three logistic regression models were trained and tested fitted to landslide occurrence data from a multi-temporal landslide inventory: (1) a model with only conventional variables, (2) a model with conventional plus landslide path dependency variables, and (3) a model with only landslide path dependency variables. We compare the model performances, differences in the number, coefficient and significance of the selected variables, and the differences in the resulting susceptibility maps. Although the landslide path dependency variables are highly significant and have impacts on the importance of other variables, the performance of the models and the susceptibility maps do not substantially differ between conventional and conventional plus path dependent models. The path dependent landslide susceptibility model, with only two explanatory variables, has lower model performance, and differently patterned susceptibility map than the two other models. A simple landslide susceptibility model using only DEM-derived variables and landslide path dependency variables performs better than the path dependent landslide susceptibility model, and almost as well as the model with conventional plus landslide path dependency variables—while avoiding the need for hard-to-measure variables such as land use or lithology. Although the predictive power of landslide path dependency variables is lower than those of the most important conventional variables, our findings provide a clear incentive to further explore landslide path dependency effects and their potential role in landslide susceptibility modelling.     

Forecasting groundwater level fluctuations for rainfall-induced landslide

Groundwater plays a critical and important role in many landslides. Heavy precipitation can raise the groundwater level within a hillslope and lead to instability. The purpose of this paper is to present a model by means of continuity equation to predict groundwater level fluctuations in hillslope in response to hourly precipitation rates. The linear reservoir method is employed to describe the travel time distribution of infiltration, and Darcy??s law is then used to establish the groundwater flux rate of control volume. The governing equation shows that the changing rate of groundwater level fluctuation can be interpreted by two new defined variables (Sink Number and Rise Number) in this study. The application of the model is demonstrated using the rainfall-induced landslide at Lu-Shan, Nantou County, Taiwan. Data from one storm event are used to calibrate the model and estimate parameters by using the heuristic algorithm. Post-storm rainfall data from another storm event are employed to verify the calibrated parameters. The contribution of this study shows that a small Sink Number results in a fast recession and a large Rise Number yields a fast rise of groundwater level. This method may be practical to have better understanding on the rainfall-induced landslide.     

滑坡成因决策树挖掘

文章介绍了构造决策树的C4.5算法。在预处理过的滑坡数据上利用决策树算法生成一组坡体稳定性规则。这些规则可作为分析及预测滑坡的重要参考。也可用来创建专家系统的知识库。其次,作者对决策树算法提出了改进,通过调整测试集的某些参数,构造了一棵局部最优的决策树。使得对于滑坡成因的分类具有更高的正确率。     

Regression models for estimating coseismic landslide displacement

Newmark's sliding-block model is widely used to estimate coseismic slope performance. Early efforts to develop simple regression models to estimate Newmark displacement were based on analysis of the small number of strong-motion records then available. The current availability of a much larger set of strong-motion records dictates that these regression equations be updated. Regression equations were generated using data derived from a collection of 2270 strong-motion records from 30 worldwide earthquakes. The regression equations predict Newmark displacement in terms of (1) critical acceleration ratio, (2) critical acceleration ratio and earthquake magnitude, (3) Arias intensity and critical acceleration, and (4) Arias intensity and critical acceleration ratio. These equations are well constrained and fit the data well (71% < R² < 88%), but they have standard deviations of about 0.5 log units, such that the range defined by the mean ± one standard deviation spans about an order of magnitude. These regression models, therefore, are not recommended for use in site-specific design, but rather for regional-scale seismic landslide hazard mapping or for rapid preliminary screening of sites.     

滑坡灾害防治技术回顾与展望

在回顾我国滑坡防治技术发展历程的基础上,分析了滑坡防治技术的现状,并对其发展前景进行了展望。      

Backward automatic calibration for three-dimensional landslide models

Back-analysis is broadly used for approaching geotechnical problems when monitoring data are available and information about the soils properties is of poor quality.For landslide stability assessment back-analysis calibration is usually carried out by time consuming trial-and-error procedure.This paper presents a new automatic Decision Support System that supports the selection of the soil parameters for three-dimensional models of landslides based on monitoring data.The method considering a pool of possible solutions,generated through permutation of soil parameters,selects the best ten configurations that are more congruent with the measured displacements.This reduces the operator biases while on the other hand allows the operator to control each step of the computation.The final selection of the preferred solution among the ten best-fitting solutions is carried out by an operator.The operator control is necessary as he may include in the final decision process all the qualitative elements that cannot be included in a qualitative analysis but nevertheless characterize a landslide dynamic as a whole epistemological subject,for example on the base of geomorphological evidence.A landslide located in Northeast Italy has been selected as example for showing the system potentiality.The proposed method is straightforward,scalable and robust and could be useful for researchers and practitioners.     

滑坡临界暴雨强度

本文介绍了几个主要暴雨滑坡集中区的临界暴雨强度,分析了各地临界暴雨强度不同的主要原因。指出前期降雨对滑坡滑动是否有明显影响,取决于滑体岩土性质及滑坡形成机制等多种因素,不可一概而论。     

Quantitative vulnerability estimation for scenario-based landslide hazards

Within the engineering profession and natural sciences, vulnerability is widely accepted to be defined as the degree of loss (or damage) to a given element or set of elements within the area affected by a threat. The value of vulnerability is expressed nondimensionally between 0 and 1. It is a fundamental component in the evaluation of landslide risk, and its accurate estimation is essential in making a reasonable prediction of the landslide consequences. Obviously, vulnerability to landslides depends not only on the characteristics of the element(s) at risk but also on the landslide intensity. This paper summarizes previous research on vulnerability to landslides and proposes a new quantitative model for vulnerability of structures and persons based on landslide intensity and resistance of exposed elements. In addition, an approximate function is suggested for estimating the vulnerability of persons in structures. Different methods for estimating the vulnerability of various elements to slow or rapid landslides are discussed. Finally, the application of the new model is illustrated through an example.     

Assessing landslide exposure in areas with limited landslide information

Landslide risk assessment is often a difficult task due to the lack of temporal data on landslides and triggering events (frequency), run-out distance, landslide magnitude and vulnerability. The probability of occurrence of landslides is often very difficult to predict, as well as the expected magnitude of events, due to the limited data availability on past landslide activity. In this paper, a qualitative procedure for assessing the exposure of elements at risk is presented for an area of the Apulia region (Italy) where no temporal information on landslide occurrence is available. Given these limitations in data availability, it was not possible to produce a reliable landslide hazard map and, consequently, a risk map. The qualitative analysis was carried out using the spatial multi-criteria evaluation method in a global information system. A landslide susceptibility composite index map and four asset index maps (physical, social, economic and environmental) were generated separately through a hierarchical procedure of standardising and weighting. The four asset index maps were combined in order to obtain a qualitative weighted assets map, which, combined with the landslide susceptibility composite index map, has provided the final qualitative landslide exposure map. The resulting map represents the spatial distribution of the exposure level in the study area; this information could be used in a preliminary stage of regional planning. In order to demonstrate how such an exposure map could be used in a basic risk assessment, a quantification of the economic losses at municipal level was carried out, and the temporal probability of landslides was estimated, on the basis of the expert knowledge. Although the proposed methodology for the exposure assessment did not consider the landslide run-out and vulnerability quantification, the results obtained allow to rank the municipalities in terms of increasing exposure and risk level and, consequently, to identify the priorities for designing appropriate landslide risk mitigation plans.     

滑坡位移的动态概率预测模型

滑坡位移预测是实现滑坡灾害预报的有效手段,文章利用回声状态网络建立动态预测模型来预测滑坡位移。与传统预测方法相比,动态模型能更好地反映出滑坡演化的动态系统本质;动态模型的建立不需要计算互信息来实现变量选择,也使得预测过程得到简化。考虑到确定性预测的局限性,文章进一步将概率预测的思想引入滑坡位移预测的研究中,提出一种动态预测模型的概率预测方法。所提动态预测模型的预测精度,在白水河和石榴树包两个滑坡位移预测的具体案例中得到了检验;而通过概率预测得到具有明确置信度水平的预测区间,从而也对滑坡状态发展变化趋势给出了更全面的描述。     

多通道滑坡位移监测仪的研制

文章提出一种滑坡表层位移监测方法,设计并研制了四通道位移监测仪器。该仪器选用特制的拉杆位移传感器完成滑坡位移传感,单片机逻辑控制实现位移监测数据动态显示,并对大量程、急速位移滑动做声光预警,监测数据自动保存。文章阐述了其工作原理及软硬件设计思路,并介绍了位移监测仪在三峡万州库区塘角村1号滑坡的野外应用实验。     

Analytic network process model for landslide hazard zonation

Various controlling factors such as lithology, slope angle, slope aspect, landuse, channel proximity etc. are generally considered for the landslide hazard assessment. Although outer dependence of these parameters to a landslide is inevitably taken into account, inter-dependence among the factors is seldom addressed. Analytic Network Process (ANP) is the multi-criteria decision making (MCDM) tool which takes into account such a complex relationship among parameters. In this research, an ANP model for landslide susceptibility is proposed, priority weights for each parameter controlling the landslide were determined, and a hazard map was prepared of an area in a fragile mountainous terrain in the eastern part of Nepal. The data used in the example were derived from published sources, aerial photographs and a topographic map. However, the procedures developed can readily incorporate additional information from more detailed investigations.     

Multitemporal UAV surveys for landslide mapping and characterization

This paper presents the preliminary results of the IPL project 196 “Development and applications of a multi-sensor drone for geohazards monitoring and mapping.” The objective of the project is to test the applicability of a multi-sensor drone for the mapping and monitoring of different types of geohazards. The Department of Earth Sciences of the University of Florence has developed a new type of drone airframe. Several survey campaigns were performed in the village of Ricasoli, in the Upper Arno river Valley (Tuscany, Italy) with the drone equipped with an optical camera to understand the possibility of this rising technology to map and characterize landslides. The aerial images were combined and analyzed using Structure-from-Motion (SfM) software. The collected data allowed an accurate reconstruction and mapping of the detected landslides. Comparative analysis of the obtained DTMs also permitted the detection of some slope portions being prone to failure and to evaluate the area and volume of the involved mass.     

滑坡预报的多元回归分析方法

在回顾了滑坡预报的概念、类型及已有滑坡预报的几种主要模型的基础上，利用多元回归分析方法，根据最小二乘法原理，建立了非线性回归预报模型，提出滑坡破坏的时间为位移．时间曲线的拐点，此方法一般适合于临滑预报。结合实例，利用黄龙西村滑坡位移．时间监测资料，采用该模型进行滑坡时间预报，和其他滑坡预报模型预报的结果接近，说明该模型具有一定的合理性。     

滑坡位移时序预测的核函数构造

获得支持向量机(SVM)背景下滑坡位移时序准确预测的关键,是构造或选择一合适的核函数。通过分析滑坡位移时序曲线特征以及不同类型Mercer核的性质,从基于核函数上的封闭运算角度,构造出支持向量机背景下预测滑坡位移时序的最佳核函数。利用3组不同特征的滑坡位移时序,对构造出的核函数进行性能检验,数值实验表明：对于典型的3组滑坡时序,LPG与MPG核的学习性能要优于简单核,且前者适合复杂位移时序的回归预测,而后者更适合规律性较强的简单时序曲线的建模预测。此外,探讨了这两种核函数下的核参数取值对模型精度的影响。     

Bagging-based machine learning algorithms for landslide susceptibility modeling

Natural Hazards - Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and...