An Integrated Multi-Level Early Warning Method for Rainfall-Induced Shallow Landslides

Rainfall-induced landslides have occurred frequently in Southwestern China since the Wenchuan earthquake,resulting in massive loss of people''s life and property. Fortunately,landslide early-warning is one of the most important tools for landslide hazard prevention and mitigation. However, the accumulation of historical data of the landslides induced by rainfall is limited in many remote mountain areas and the stability of the slope is easily affected by human engineering activities and environmental changes, leading to difficulties to accurately realize early warning of landslide hazards by statistical methods. The proposed warning method is divided into rainfall warning component and deformation warning component because the deformation induced by rainfall has the characteristic of hysteretic nature. Rainfall, tilted angle and crack width are chosen as monitoring indexes. Rainfall grade level that contains rainfall intensity and duration information is graded according to the variation of the safety factor calculated by 3-D finite difference numerical simulation method, and then is applied using the strength reduction method and unascertained information theory to obtain the deformation grade level of several monitored points. Finally, based on the system reliability theory, we establish a comprehensive landslide warning level method that provides four early warning levels to reflect the safety factor reductions during and post rainfall events. The application of this method at a landslide site yield generally satisfactory results and provide a new method for performing multi-index and multi-level landslide early warnings.     

Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.     

Approaches for defining thresholds and return periods for rainfall‐triggered shallow landslides

Probabilistic thresholds for triggering shallow landslides by rainfall are developed using two approaches: a logistic regression model and Iverson's physically based model. Both approaches are applied to a 180 km² area in northern Italy. For the physically based model a Monte Carlo approach is used to obtain probabilities of slope failure associated with differing combinations of rainfall intensity and duration as well as differing topographic settings. For the logistic regression model hourly and daily rainfall data and split‐sample testing are used to explore the effect of antecedent rainfall on triggering thresholds. It is demonstrated that both the statistical and physically based models provide stochastic thresholds that express the probability of landslide triggering. The resulting thresholds are comparable, even though the two approaches are conceptually different. The physically based model also provides an estimate of the percentage of potentially unstable areas in which failure can be triggered with a certain probability. The return period of rainfall responsible for landslide triggering is studied by using a Gumbel scaling model of rainfall intensity–duration–frequency curves. It is demonstrated that antecedent rainfall must be taken into account in landslide forecasting, and a method is proposed to correct the rainfall return period by filtering the rainfall maxima with a fixed threshold of antecedent rainfall. This correction produces an increase of the return periods, especially for rainstorms of short duration. Copyright © 2009 John Wiley & Sons, Ltd.     

基于时序InSAR技术的新磨村滑坡发生前后变形研究

基于时序InSAR技术,并结合现场调查,采用新磨村滑坡发生前30景SAR数据和滑坡发生后36景SAR数据对滑坡及周边区域形变进行监测,结果表明:①新磨村滑坡发生前,坡脚未探测到明显的变形,但滑源区有明显的变形迹象,主要变形发生2016年和2017年,滑源区坡体经历了“缓慢变形-加速-暂时稳定-再加速-滑坡发生”过程;②新磨村滑坡发生后,在坡脚堆积区东南部形成一个不稳定变形体,累积变形量较大,雨季有加快变形趋势,尚未达到稳定状态;③根据新磨村滑坡前、后变形规律,滑坡发生前、后地表变形受降雨影响明显。     

基于人工边坡稳定性的降雨型滑坡预警预报模型设计与应用

由削坡建房遗留的人工边坡存在大量滑坡隐患问题,在降雨引发土质边坡自身动力变化分析条件下,以稳定性评价建模为基础,提出降雨型滑坡动力学预警预报模型.文中以广东省梅州市花岗岩地区为例,使用GIS技术构建了1727个预警分析单元,并进行关键地质环境因子赋值及与气象站点数据关联;按坡高、坡度等参数,分别构建16个边坡失稳动力学...     

Prediction of landslide occurrence based on slope-instability analysis and hydrological model simulation

Hillslope failure usually occurs as soil resistance deteriorates in the presence of the acting stress developed by a rising groundwater level during rainstorms. The present study adopted a slope-instability analysis and a hydrological model for landslide prediction during heavy rainstorms. Variation of the groundwater table on hillslope was simulated by using the hydrological model and then the temporal groundwater level at each grid was substituted into the slope-instability analysis to determine the instability of the grids in watersheds for prediction of massive landslides.Hydrological records from two landslide-prone areas in northern Taiwan were collected. Digital elevation model was adopted to obtain the geomorphologic factors required for the slope-instability analysis and the hydrological model. The spatial distribution of soil thickness required for performing the infinite slope model was estimated by using a wetness index. Results showed that the temporal variation of the percentage of unstable grids in the study watersheds basically followed the variation of rainfall hyetographs. The percentage of the unstable grids reached a maximum value when the centroid of the hyetograph passed. A comparison between the landslide records and the model analytical results revealed that a massive landslide might occur if more than 50% of the grids in the subwatershed were classified as unstable in the study areas. The predicted time and location of landslide occurrence were consistent with those obtained from field investigations. It is therefore considered promising to apply the developed analytical method for landslide warning to alleviate the loss of lives and property.     

四川德阳地区龙风水库西岸滑坡稳定性分析及防治

本文在对四川德阳地区龙风水库西岸滑坡的地质地貌条件及变形迹象进行深入调查的基础上,对该滑坡的主要成因及稳定性进行了分析与评价,并有针对性地提出了工程防治方案。研究表明,疏松透水的土层、较强的降雨以及人类耕作活动是导致滑坡发生的主要因素。在天然状态下该滑坡稳定或基本稳定;在天然＋地震状态下,该滑坡欠稳定或不稳定;在连续高强度降雨状态下,该滑坡不稳定。故采取截、排水及抗滑桩等工程措施是防治该滑坡的有效方案。     

Rainfall characteristics for shallow landsliding in Seattle,Washington, USA

Shallow landsliding in the Seattle, Washington, area, has caused the occasional loss of human life and millions of dollars in damage to property. The effective management of the hazard requires an understanding of the rainfall conditions that result in landslides. We present an empirical approach to quantify the antecedent moisture conditions and rainstorm intensity and duration that have triggered shallow landsliding using 25 years of hourly rainfall data and a complementary record of landslide occurrence. Our approach combines a simple water balance to estimate the antecedent moisture conditions of hillslope materials and a rainfall intensity–duration threshold to identify periods when shallow landsliding can be expected. The water balance is calibrated with field‐monitoring data and combined with the rainfall intensity–duration threshold using a decision tree. Results are cast in terms of a hypothetical landslide warning system. Two widespread landslide events are correctly identified by the warning scheme; however, it is less accurate for more isolated landsliding. Copyright © 2005 John Wiley & Sons, Ltd.     

降雨对无支挡结构下隧道-滑坡正交体系的作用机理研究

通过大型地质力学模型试验,研究在无支挡结构下降雨对隧道-滑坡正交体系的作用机理。主要研究不同降雨阶段,隧道-滑坡正交体系下隧道纵向应变的变化特征及隧道不同横断面环向应力变化特点,并重点分析在不同降雨阶段,不同位置及其不同横断面滑体的位移变化特征。试验结果表明:(1)降雨可导致坡体横断面断裂而出现新的滑移面,从而导致坡体失稳。(2)在隧道-滑坡正交体系下,随着雨水的下渗及滑体土样含水率不断增大,隧道局部应变有明显突变,且山侧纵向应变比河侧应变要大。(3)在隧道-滑坡正交体系下,降雨使得隧道环向应力呈不均匀变化:隧道的底部应力大于顶部应力,山侧应力大于河侧应力,表明降雨可导致滑体蠕动或局部滑移,引起隧道不均匀受力及变形,这对隧道结构的安全非常不利。本次试验可为雨水充沛区的滑坡及隧道抗滑设计提供一定参考。     

Analysing the Relationship Between Typhoon‐Triggered Landslides and Critical Rainfall Conditions

Many landslides are triggered by rainfall. Previous studies of the relationship between landslides and rainfall have concentrated on deriving minimum rainfall thresholds that are likely to trigger landslides. Though useful, these minimum thresholds derived from a log–log plot do not offer any measure of confidence in a landslide monitoring or warning system. This study presents a new and innovative method for incorporating rainfall into landslide modelling and prediction. The method involves three steps: compiling radar reflectivity data in a QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system during a typhoon (tropical hurricane) event, estimating rainfall from radar data and using rainfall intensity and rainfall duration as explanatory variables to develop a landslide logit model. Given the logit model, this paper discusses ways in which the model can be used for computing probabilities of landslide occurrence for a real‐time monitoring system or a warning system, and for delineating and mapping landslides. Copyright © 2007 John Wiley & Sons, Ltd.     

Earthquake-reactivated landslide scenarios in Southern Italy based on spectral-matching input analysis

The Tyrrhenian portion of the Calabria region (southern Italy) is particularly prone to landslides as a consequence of intense morphodynamic processes. These processes affect the slopes that are composed of highly jointed metamorphic rock masses. Moreover, the frequent intense rainfalls and the up to Mw 7.0 regional earthquakes represent the main landslide triggering factors. An area of approximately $45\,\hbox {km}^{2}$ was selected as a test site in the context of a regional project aimed at reconstructing possible earthquake-reactivated landslide scenarios (i.e., referred to already existing landslide masses). An inventory map led to the identification of 175 landslides, including rock slides, earth slides and rock falls. Ground-motion scenarios based on a spectral-matching method were derived to evaluate the expected earthquake-induced displacements of the existing landslides. Naturally recorded acceleration time histories were selected from international ground-motion databases based on a similarity index and considered representative of the seismological features of the considered seismic sources (i.e., epicentral distance, magnitude, focal mechanism). Spectral attenuation was considered, according to well-established attenuation laws, to define the expected response spectrum at the outcropping bedrock corresponding to each existing landslide. Subsequently, the selected natural records were modified to guarantee spectral matching with the attenuated response spectra at each landslide site. The derived time histories were used to compute co-seismic displacements via the classic Newmark’s sliding-block method. Different scenarios of co-seismic landslide displacements or collapse were generated for different pore-water pressure hypotheses. The strongest $\hbox {Mw}>6$ seismic scenario (Messina Straits seismogenic source) indicated an exceedance probability of earthquake-induced co-seismic landslide collapse varying from 20 to 55 % with the increasing severity of the pore-water pressures. This probability corresponds to a percentage of co-seismic landslide displacements up to 40 % of the total inventoried landslides. The exceedance probability indicated that co-seismic landslide collapse drops below 20 % for $\hbox {Mw}<6$ seismic scenarios. In contrast, if a uniform probability is assumed for the seismic action occurrence, i.e., return periods of 475 and 2,475 years, the total percentage of landslide co-seismic displacements could be as high as 70 and 90 %, respectively, for the considered pore-water pressures.     

A new method for spatio-temporal prediction of rainfall-induced landslide

1 Introduction The landslides influences on the human society have become an environment difficult problem not able to be neglected, and according to the priority of harms, harms of landslides are only smaller than those from earthquakes in all sorts of natural hazards[1]. Landslide is part of rock mass, soil mass or their compound mass slides downward along a certain slid- ing surface under the actions of inner and external dy- namics, and it is one severe instability phenomenon of rock and s…     

A spatiotemporal probabilistic modelling of storm‐induced shallow landsliding using aerial photographs and logistic regression

Landslides constitute one of the major natural hazards that could cause significant losses of life and property. Mapping or delineating areas prone to landsliding is therefore essential for land‐use activities and management decision making in hilly or mountainous regions. A landslide hazard map can be constructed by a qualitative combination of maps of site conditions, including geology, topography and geomorphology, by statistical methods through correlating landslide occurrence with geologic and geomorphic factors, or by using safety factors from stability analysis. A landslide hazard map should provide information on both the spatial and temporal probabilities of landsliding in a certain area. However, most previous studies have focused on susceptibility mapping, rather than on hazard mapping in a spatiotemporal context. This study aims at developing a predictive model, based on both quasi‐static and dynamic variables, to determine the probability of landsliding in terms of space and time. The study area selected is about 13 km² in North Lantau, Hong Kong. The source areas of the landslides caused by the rainstorms of 18 July 1992 and 4–5 November 1993 were interpreted from multi‐temporal aerial photographs. Landslide data, lithology, digital elevation model data, land cover, and rainfall data were digitized into a geographic information system database. A logistic regression model was developed using lithology, slope gradient, slope aspect, elevation, slope shape, land cover, and rolling 24 h rainfall as independent variables, since the dependent variable could be expressed in a dichotomous way. This model achieved an overall accuracy of 87·2%, with 89·5% of landslide grid cells correctly classified and found to be performing satisfactorily. The model was then applied to rainfalls of a variety of periods of return, to predict the probability of landsliding on natural slopes in space and time. It is observed that the modelling techniques described here are useful for predicting the spatiotemporal probability of landsliding and can be used by land‐use planners to develop effective management strategies. Copyright © 2003 John Wiley & Sons, Ltd.     

基于降雨入渗的Newmark模型改进及地震滑坡危险性预测研究

滑坡是一种破坏性非常强的地质灾害,其中地震与降雨均为诱导滑坡发生的关键因素。从降雨期间发生地震的角度考虑,基于Green-Ampt降雨入渗模型对Newmark模型进行改进,推导两因素耦合作用下的边坡安全系数F_S。以云南省鲁甸县某一区域为例,分别开展无降雨、降雨无积水与降雨积水三种情况下的地震滑坡危险性预测及坡度与入渗深度因子对位移影响分析。通过比较上述三种情况,得到研究区域内的Newmark累积位移分布及危险性区划。结果表明：与未降雨情况相比,后两种情况下地震滑坡高危险程度区域面积占比计算区域随着降雨时间的增加从1%分别提高至9%、12%,滑坡低危险程度区域面积从51%分别降低至35%、33%;坡度值与入渗深度值越大,滑坡位移越大,危险性越高。Newmark改进模型充分考虑了降雨对地震滑坡产生的促进作用,能更好地反映出研究区每个场点相对的滑坡危险性,对滑坡危险性预测具有一定指导意义。     

Landslide activation behaviour illuminated by electrical resistance monitoring

A common factor in landslide activation (or reactivation) is subsurface moisture and associated pore pressure variations linked to rainfall. Monitoring of these subsurface hydrogeological processes is necessary to improve our understanding of water‐induced landslide activation. Geophysical approaches, electrical methods in particular, are increasingly being applied to landslide monitoring because they provide non‐invasive spatial information in heterogeneous subsurface environments that can be difficult to characterise using surface observations or intrusive sampling alone. Electrical techniques are sensitive to changing subsurface moisture conditions, and have proven to be a useful tool for investigating the hydrogeology of natural and engineered slopes. The objectives of this investigation were to further develop electrical resistance monitoring for slope stability assessment, and to validate the approach at an intermittently‐active UK landslide system to advance the understanding of complex landslide activation mechanisms. A long‐term transfer resistance dataset was collected from a grid of electrodes to allow spatial monitoring of the landslide. These data were interpreted using a synthesis of rainfall, temperature, GPS and piezometric records. The resistance data were corrected for seasonal temperature variations and electrode movements were monitored, as these processes were shown to mask moisture related changes. Results reveal that resistance monitoring is sensitive to soil moisture accumulation, including changes in piezometric levels, and can be used to study the principal activation mechanism of slow‐moving shallow earthflows. Spatial monitoring using resistance maps was shown to be particularly valuable as it revealed the evolution of subsurface moisture distribution, in the lead up to landslide activation. Key benefits of this approach are that it provides a simple, rapid and non‐invasive means of spatially monitoring subsurface moisture dynamics linked to landslide activation at high‐temporal resolution. Crucially, it provides a means of monitoring subsurface hydraulic changes in the build‐up to slope failure, thereby contributing to early warning of landslide events. Copyright © 2018 John Wiley & Sons, Ltd.     

Analysis of Ground Deformation Detected Using the SBAS-DInSAR Technique in Umbria, Central Italy

Ground deformation affecting the Umbria region (central Italy) in the 9-year period from 1992 to 2000 was investigated through multi-temporal Differential Synthetic Aperture Radar Interferometry (DInSAR). For the purpose, the Small BAseline Subset (SBAS) technique was adopted, which allows studying the temporal evolution of the detected deformation at two spatial scales: a low-resolution (regional) scale, and a full-resolution (local) scale. For the analysis, SAR data acquired by the European Remote Sensing (ERS-1/2) satellites along ascending and descending orbits were used. The detected deformation was analysed to investigate its relevance to geophysical, geomorphologic, and human-induced processes that may result in hazardous conditions to the population of Umbria. Low-resolution deformation data were used to: (i) determine the amount of displacement caused by the Umbria-Marche earthquake sequence from September 1997 to April 1998 in the Foligno area, (ii) determine the number and percentage of the known landslides that can be monitored by the DInSAR technology in the investigated area, and (iii) identify and measure subsidence induced by exploitation of a confined aquifer in the Valle Umbra. Results indicate that earthquakes moved through the Foligno area westwards up to 3.9 cm and with an uplift reaching 1.7 cm. Intersection in a GIS of the low-resolution deformation maps with a detailed landslide inventory map allowed the determination that the portion of landslides that can be monitored by the SBAS-DInSAR technique in Umbria ranges from 2.7% to 3.4%, and the percentage of the total landslide area ranges from 10.4% to 12.8%. In the Valle Umbra, a dependency was found between the time and the amount of detected ground deformation, and the record of water withdrawal. The full-resolution deformation data were used to investigate the movement of the Ivancich landslide, in the Assisi Municipality. Joint analysis of the spatial and the temporal characteristics of the ground displacement allowed the formulation of a hypothesis on the landslide geometry and deformation pattern.     

Comparing threshold definition techniques for rainfall‐induced landslides: A national assessment using radar rainfall

Translational landslides and debris flows are often initiated during intense or prolonged rainfall. Empirical thresholds aim to classify the rain conditions that are commonly associated with landslide occurrence and therefore improve understating of these hazards and predictive ability. Objective techniques that are used to determine these thresholds are likely to be affected by the length of the rain record used, yet this is not routinely considered. Moreover, remotely sensed spatially continuous rainfall observations are under‐exploited. This study compares and evaluates the effect of rain record length on two objective threshold selection techniques in a national assessment of Scotland using weather radar data. Thresholds selected by ‘threat score’ are sensitive to rain record length whereas, in a first application to landslides, ‘optimal point’ (OP) thresholds prove relatively consistent. OP thresholds increase landslide detection and may therefore be applicable in early‐warning systems. Thresholds combining 1‐ and 12‐day antecedence variables best distinguish landslide initiation conditions and indicate that Scottish landslides may be initiated by lower rain accumulation and intensities than previously thought. © 2017 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Tsunami Probability in the Caribbean Region

We calculated tsunami runup probability (in excess of 0.5 m) at coastal sites throughout the Caribbean region. We applied a Poissonian probability model because of the variety of uncorrelated tsunami sources in the region. Coastlines were discretized into 20 km by 20 km cells, and the mean tsunami runup rate was determined for each cell. The remarkable ~500-year empirical record compiled by O’Loughlin and Lander (2003) was used to calculate an empirical tsunami probability map, the first of three constructed for this study. However, it is unclear whether the 500-year record is complete, so we conducted a seismic moment-balance exercise using a finite-element model of the Caribbean-North American plate boundaries and the earthquake catalog, and found that moment could be balanced if the seismic coupling coefficient is c = 0.32. Modeled moment release was therefore used to generate synthetic earthquake sequences to calculate 50 tsunami runup scenarios for 500-year periods. We made a second probability map from numerically-calculated runup rates in each cell. Differences between the first two probability maps based on empirical and numerical-modeled rates suggest that each captured different aspects of tsunami generation; the empirical model may be deficient in primary plate-boundary events, whereas numerical model rates lack backarc fault and landslide sources. We thus prepared a third probability map using Bayesian likelihood functions derived from the empirical and numerical rate models and their attendant uncertainty to weight a range of rates at each 20 km by 20 km coastal cell. Our best-estimate map gives a range of 30-year runup probability from 0–30% regionally.     

Rainfall‐induced landslide hazard assessment using artificial neural networks

In Japan, landslides triggered by heavy rainfall tend to occur during the annual rainy season from early June until the middle of July; these landslides constitute a major hazard causing significant property damage and loss of life. This paper proposes the use of back propagation neural networks (BPNN) to predict the probability of landslide occurrence for a scenario of heavy rainfall in the Minamata area of southern Kyushu Island, Japan. All of the landslides were detected from aerial photographs taken in 1999, 2001 and 2003, and a geospatial database of lithology, topography, soil characteristics, land use and precipitation was constructed using geographical information systems (GIS). The training sample consists of 602 cells that include landslide activity and 1600 cells in stable areas. Using the trained BPNN with 49 input nodes, three hidden layers, and one output node, 239 589 cells were processed to produce a map of landslide probability for a maximum daily precipitation of 329 mm and a maximum cumulative precipitation of 581 mm for an incessant, intense rainfall event in the future. The resultant hazard map was classified into four hazard levels; it can be referenced for land‐use planning and decision‐making for community development. Copyright © 2005 John Wiley & Sons, Ltd.