Landslide hazard and community-based risk reduction effort in Karanganyar and the surrounding area, central Java, Indonesia

Karanganyar and the surrounding area are situated in a dynamic volcanic arc region, where landslide frequently occurs during the rainy season. The rain-induced landslide disasters have been resulting in 65 fatalities and a substantial socioeconomical loss in last December 2007. Again, in early February 2009, 6 more people died, hundreds of people temporary evacuated and tens of houses damaged due to the rain-induced landslide. Accordingly, inter-disciplinary approach for geological, geotechnical and social investigations were undertaken with the goal for improving community resilience in the landslide vulnerable villages. Landslide hazard mapping and community-based landslide mitigation were conducted to reduce the risk of landslides. The hazard mapping was carried out based on the susceptibility assessment with respect to the conditions of slope inclination, types and engineering properties of lithology/soil as well as the types of landuse. All of those parameters were analyzed by applying weighing and scoring system which were calculated by semi qualitative approach (Analytical Hierarchical Process). It was found that the weathered andesitic-steep slope (steeper than 30o) was identified as the highest susceptible slope for rapid landslide, whilst the gentle colluvial slope with inter-stratification of tuffaceous clay-silt was found to be the susceptible slope for creeping. Finally, a programme for landslide risk reduction and control were developed with special emphasize on community-based landslide mitigation and early warning system. It should be highlighted that the social approach needs to be properly addressed in order to guarantee the effectiveness of landslide risk reduction.     

基于多重分形特征和分项组合预测联合响应的滑坡预警预测研究

为准确掌握滑坡变形发展规律,基于滑坡变形监测成果构建滑坡预警预测模型,即先利用MF-DFA模型开展滑坡变形数据的多重分形特征分析,并进一步利用M-K分析构建双重判据（Δa指标判据和Δf(a)指标判据）进行滑坡预警研究;另外,在利用集成经验模态分解法对滑坡变形数据信息进行分离处理基础上,通过GOA-RNN-CT模型实现滑坡变形的分项组合预测。结果表明,h(q)值随波动函数q值减小而减小,说明滑坡变形数据具有多重分形特征,且预警分级研究表明,滑坡预警等级为Ⅱ级,即滑坡变形趋向不利方向发展;同时,通过变形预测分析认为,分项组合预测在滑坡变形预测中具有较优的预测效果和稳定性,且外推预测结果显示,滑坡变形会继续增加;最后,将多重分形特征研究结果和变形预测分析结果进行联合响应综合得出,滑坡现有预警等级相对不利,且后续变形仍会进一步增加,趋向不稳定方向发展,建议对滑坡采取必要防治措施。     

四川省滑坡灾害气象预警模型建立与验证

四川省滑坡灾害严重,特别是2008年之后,灾情显著加剧,如何预防滑坡灾害是保护人民生命财产安全的有效途径。滑坡灾害的预警模型研究是滑坡灾害预防领域的核心课题。本文对四川省滑坡灾害危险性进行了评价,并开展了滑坡灾害气象风险预警模型研究。①以确定性系数的方法量化坡度、地形起伏度、水文地质岩性、植被覆盖度、地震烈度和年均降雨量因子,建立逻辑回归模型,定量地进行四川省滑坡灾害危险性区划,并对结果进行验证。结果表明,四川省滑坡灾害高危险性区域成“Y”字型分布,此外川中、川东北地区滑坡灾害危险性也非常高,这与四川省滑坡灾害的空间分布情况相符。②在前期滑坡灾害与降雨量统计分析、滑坡灾害危险性评价的基础上,以滑坡灾害危险性评价为静态因子,日降雨量数据为动态因子,通过逻辑回归模型的结果,确定以当日降雨量概率化值、滑坡灾害危险性值、前一日降雨概率化值、前两日降雨概率化值、前三日降雨概率化值为临灾模型影响因子,各因子对预警结果影响程度按上述顺序递减,建立了地质-气象耦合的临灾气象预警模型。通过检验区数据对模型的检验表明,该预警模型能成功预警80%以上的滑坡灾害;通过滑坡灾害群发个例检验发现,该预警模型与四川省现用模型相比,预警区域明显减小,空报率和漏报率显著降低。     

基于滑坡灾害预警分级的应急处置措施

滑坡灾害应急处置能力是地质灾害减灾防灾的重要方面。目前,基于滑坡灾害预测和预警分级成果,系统性的应急措施分类研究还鲜有展开,因此,以三峡库区白水河滑坡为例,运用时间序列加法模型将滑坡累计位移分解为趋势项位移与周期项位移,并分别应用多项式拟合及自回归（AR）模型对2个分量进行预测,在此结果上采用聚类分析方法将滑坡变形分为匀速变形与加速变形阶段,综合判断滑坡灾害预警等级,开展了针对滑坡预警分级的应急措施研究。结果表明:白水河滑坡预警等级主要为蓝色和黄色2种类型,对处于不同的预警等级下的滑坡,可根据滑坡变形特征快速决策,基于滑坡灾害预测和预警分级结果能更有效地指导滑坡应急处置。      

A Real-time Monitoring and Early Warning System for Landslides in Southwest China

Landslides not only cause property losses,but also kill and injure large numbers of people every year in the mountainous areas. These losses and casualties may be avoided to some extent by early warning systems for landslides. In this paper, a realtime monitoring network and a computer-aided automatic early warning system(EWS) are presented with details of their design and an example of application in the Longjingwan landslide, Kaiyang County, Guizhou Province. Then, according to principle simple method of landslide prediction, the setting of alarm levels and the design of appropriate counter-measures are presented. A four-level early warning system(Zero, Outlook, Attention and Warning) has been adopted, and the velocity threshold was selected as the main warning threshold for the landslide occurrence, but expert judgment is included in the EWS to avoid false alarms. A case study shows the applicability and reliability for landslide risk management, and recommendations are presented for other similar projects.     

Probability Prediction Model for Landslide Occurrences in Xi’an,Shaanxi Province,China

Landslides are increasing since the 1980s in Xi’an, Shaanxi Province, China. This is due to the increase of the frequency and intensity of precipitation caused by complex geological structures, the presence of steep landforms, seasonal heavy rainfall, and the intensifcation of human activities. In this study, we propose a landslide prediction model based on the analysis of intraday rainfall （IR） and antecedent effective rainfall （AER）. Primarily, the number of days and degressive index of the antecedent effective rainfall which affected landslide occurrences in the areas around Qin Mountains, Li Mountains and Loess Tableland was established. Secondly, the antecedent effective rainfall and intraday rainfall were calculated from weather data which were used to construct critical thresholds for the 10%, 50% and 90% probabilities for future landslide occurrences in Qin Mountain, Li Mountain and Loess Tableland. Finally, the regions corresponding to different warning levels were identified based on the relationship between precipitation and the threshold, that is; “A” region is safe, “B” region is on watch alert, “C” region is on warning alert and “D” region is on severe warning alert. Using this model, a warning program is proposed which can predict rainfall-induced landslides by means of real-time rain gauge data and real-time geo-hazard alert and disaster response programs. Sixteen rain gauges were installed in the Xi’an region by keeping in accordance with the regional geology and landslide risks. Based on the data from gauges, this model accurately achieves the objectives of conducting real-time monitoring as well as providing early warnings of landslides in the Xi’an region.     

Panzhihua airport landslide (Oct. 3rd 2009) and an emergency monitoring and warning system based on the internet of things

Panzhihua city(26°05’-27°21’N,101°08’102°15’E),located in a mountainous area,is one of the large cities in Sichuan province,China.A landslide occurred in the filling body of the eastern part of the Panzhihua airport on October 3,2009(hereafter called the 10.3 landslide).We conducted field survey on the landslide and adopted emergency monitoring and warning models based on the Internet of Things(IoT) to estimate the losses from the disaster and to prevent a secondary disaster from occurring.The results showed that four major features of the airport site had contributed to the landslide,i.e,high altitude,huge amount of filling rocks,deep backfilling and great difficulty of backfilling.The deformation process of the landslide had six stages and the unstable geological structure of high fillings and an earthquake were the main causes of the landslide.We adopted relative displacement sensing technology and Global System for Mobile Communications(GSM) technology to achieve remote,real-time and unattended monitoring of ground cracks in the landslide.The monitoring system,including five extensometers with measuring ranges of 200,450 and 700 mm,was continuously working for 17 months and released 7 warning signals with an average warning time of about 26 hours.At 10 am on 6 December 2009,the system issued a warning and on-site workers were evacuated and equipment protected immediately.At 2:20 pm on 7 December,a medium-scale collapse occurred at the No.5 monitoring site,which justified the alarm and proved the reliability and efficiency of the monitoring system.     

台风-非台风降雨型滑坡的多时段临界雨量值预测模型

统计确定临界降雨量是滑坡早期预警常用的方法。东南沿海地区台风暴雨不同于一般降雨, 常引发滑坡灾害, 从而威胁沿海地区人民生命财产安全。为了建立台风和非台风降雨型滑坡临界降雨量预测模型, 以浙江丽水市为例, 基于2010-2020年台风暴雨、非台风降雨诱发滑坡与降雨量的统计, 构建了丽水市滑坡发生概率和有效降雨量的关系, 提出了多时长临界降雨量预测模型, 并开展了台风和非台风降雨型滑坡预测模型结果的对比分析。结果表明, 非台风降雨与台风暴雨之间雨型和雨量差异是导致丽水市内2类降雨滑坡预测模型差异的主要原因; 以多时长预测模型确定的临界雨量值法和有效降雨天数更加符合丽水市降雨型滑坡的预测预报, 且预测精度相比于传统相关性分析法更高。研究成果对于开发区域降雨型滑坡预测模型具有理论意义, 对我国东南沿海地区汛期滑坡早期预警具有重要实际意义。      

山东省典型地区地质灾害野外科学观测效果初探

重点选取山东省具有代表性的典型地质灾害易发区：济南市历城区阁老滑坡、青岛崂山区青山滑坡以及临沂市城区岩溶塌陷易发区,进行地质灾害野外科学观测和预警预报研究,并提出下一步研究方向,以期提高山东省预警预报的可信度和准确度,并将其作为地质灾害自动化观测的试验点和示范点在全省推广应用。     

Hazard and population vulnerability analysis: a step towards landslide risk assessment

In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlán, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility,magnitude(area-velocity ratio) and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory; the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources(Google Earth,aerial photographs and historical information).Estimations of landslide susceptibility were determined by combining four statistical techniques:(i) logistic regression,(ii) quadratic discriminant analysis,(iii) linear discriminant analysis, and(iv)neuronal networks. A Digital Elevation Model(DEM)of 10 m spatial resolution was used to extract the slope angle, aspect, curvature, elevation and relief.These factors, in addition to land cover, lithology anddistance to faults, were used as explanatory variables for the susceptibility models. Additionally, a Poisson model was used to estimate landslide temporal frequency, at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements. Then,due to the complexity of evaluating it, vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment(SAVE) model which considered levels of exposure, sensitivity and lack of resilience. Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas. It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments.Consequently, levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.     

湖北省黄冈市降雨型滑坡气象预警判据

黄冈市是湖北省汛期地质灾害频发区之一, 地质灾害类型以滑坡为主, 其中75%为降雨型滑坡。通过统计分析黄冈市近10年滑坡与降雨的相关关系, 在考虑黄冈市地质灾害易发性分区基础上, 研究黄冈市降雨型滑坡的降雨阈值, 利用逻辑回归模型建立滑坡发生的概率预测模型, 再针对不同等级易发区提出对应的气象预警判据。最后以历史降雨及其滑坡事件检验预警判据的合理性与可信度。结果表明, 所建立的气象预警判据在时间尺度上由以往依托气象部门的中长期预警精细到了24 h的短临预警, 在空间尺度上确定了不同等级易发区的降雨型滑坡气象预警判据。预警准确率大幅提升, 显著提高了黄冈市降雨型滑坡气象预警精度, 可为临灾转移提供精细化的技术指导, 有效降低降雨型滑坡灾害带来的生命财产损失。      

Probabilistic rainfall thresholds in Chibo,India: estimation and validation using monitoring system

The Himalayan region has been severely affected by landslides especially during the monsoons. In particular, Kalimpong region in Darjeeling Himalayas has recorded several landslides and has caused significant loss of life, property and agricultural land. The study region, Chibo has experienced several landslides in the past which were mainly debris and earth slide. Globally, several types of rainfall thresholds have been used to determine rainfall-induced landslide incidents. In this paper, probabilistic thresholds have been defined as it would provide a better understanding compared to deterministic thresholds which provide binary results, i.e., either landslide or no landslide for a particular rainfall event. Not much research has been carried out towards validation of rainfall thresholds using an effective and robust monitoring system. The thresholds are then validated using a reliable system utilizing Microelectromechanical Systems(MEMS) tilt sensor and volumetric water content sensor installed in the region. The system measures the tilt of the instrument which is installed at shallow depths and is ideal for an early warning system for shallow landslides. The change in observed tilt angles due to rainfall would give an understanding of the applicability of the probabilistic model. The probabilities determined using Bayes' theorem have been calculated using the rainfall parameters and landslide data in 2010-2016. The rainfall values were collected from an automatic rain gauge setup near the Chibo region. The probabilities were validated using the MEMS based monitoring system setup in Chibo for the monsoon season of 2017. This is the first attempt to determine probabilities and validate it with a robust and effective monitoring system in Darjeeling Himalayas. This study would help in developing an early warning system for regions where the installation of monitoring systems may not be feasible.     

Numerical simulation of a high-speed landslide in Chenjiaba,Beichuan, China

High-speed landslide is a catastrophic geological disaster in the mountainous area of southwest China. To predict the movement process of landslide reactivation in Chenjiaba town, Beichuan county, Sichuan province, China, we simulated the movement process of two landslide failures in Chenjiaba via rapid mass movement simulation and unmanned aerial vehicle images(UAV), and obtained the movement characteristic parameters of the landslides. According to a back analysis, the most remarkable fitting rheological parameters were friction coefficient(μ=0.18) and turbulence(). The parameter of landslide pressure was applied as the zoning index of landslide hazard to obtain the influence zone and hazard zoning map of the Chenjiaba landslide. Results show that the Duba River was blocked quickly with a landslide accumulation at the maximum height of 44.14 mwhen the Chenjiaba deposits lost stability. The hazard zoning map indicated that the landslide hazard degree is positively correlated with the slope.This landslide assessment is a quantitative hazard assessment method based on a landslide movement process and is suitable for high-speed landslide. Such method can provide a scientific basis for urban construction and planning in the landslide hazard area to avoid hazards effectively.     

Characteristics and failure mechanism of an ancient earthquake-induced landslide with an extremely wide distribution area

The Lamuajue landslide is located in Lamuajue village on the right bank of the Meigu River,Sichuan Province, China. This landslide is an ancient landslide with an extremely wide distribution area,covering an area of 19 km~2 with a maximum width of5.5 km and an estimated residual volume of 3×10~8 m~3.The objectives of this study were to identify the characteristics and failure mechanism of this landslide. In this study,based on field investigations,aerial photography, and profile surveys, the boundary,lithology, structure of the strata, and characteristics of the landslide deposits were determined. A gently angled weak interlayer consisting of shale was the main factor contributing to the occurrence of the Lamuajue landslide. The deposition area can be divided into three zones: zone A is an avalanche deposition area mainly composed of blocks,fragments, and debris with diameters ranging from0.1 m to 3 m; zone B is a residual integrated rock mass deposition area with large blocks,boulders and "fake bedrock"; and zone C is a deposition zone of limestone blocks and fragments. Three types of failure mechanism were analyzed and combined to explain the Lamuajue landslide based on the features of the accumulation area. First, a shattering-sliding mechanism caused by earthquakes in zone A. Second,a sliding mechanism along the weak intercalation caused by gravity and water in zone B. Third,a shattering-ejection mechanism generated by earthquakes in zone C. The results provide a distinctive case for the study of gigantic landslides induced by earthquakes, which is very important for understanding and assessing ancient earthquakeinduced landslides.     

Development of community-based landslide early warning system in the earthquake-affected areas of Nepal Himalaya

In the central Nepal Himalaya, landslides form the major natural hazards annually resulting in many casualties and damage. Structural as well as non-structural measures are in place to minimize the risk of landslide hazard. To reduce the landslide risk,a Landslide Early Warning System(LEWS) as a nonstructural measure has been piloted at Sundrawati village(Kalinchowk rural municipality, Dolakha district) to identify its effectiveness. Intensive discussions with stakeholders, aided by landslide susceptibility map, resulted in a better understanding of surface dynamics and the relationship between rainfall and surface movement. This led to the development of a LEWS comprised of extensometers,soil moisture sensors, rain gauge stations, and solar panels as an energy source that blows siren receiving signals via a micro-controller and interfacing circuit.The data generated through the system is transmitted via a Global System for Mobile Communications(GSM) network to responsible organizations in realtime to circulate the warning to local residents. This LEWS is user-friendly and can be easily operated by a community. The successful pilot early warning system has saved 495 people from 117 households in August 2018.However,landslide monitoring and dissemination of warning information remains a complex process where technical and communications skill should work closely together.     

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SBAS-InSAR的矿区采空区潜在滑坡综合识别方法

针对位于山区且受大量采空区影响的边坡,利用传统测量方法监测耗费人力、物力且光学遥感难以定量识别其是否为潜在滑坡的问题,本文提出一种融合研究区小基线集（SBAS-InSAR）地表监测数据、坡度及坡向的识别方法。通过SBAS-InSAR技术获得研究区地表雷达视线（LOS）方向形变速率,将其转化为垂直方向形变速率,并根据研究区DEM建立坡度及坡向分析图,根据不同山体的坡度、坡向找到易发生滑坡的区域,融入该区域垂直方向的时序形变速率,对其进行滑坡识别。实验表明：卡房镇周边受采空区的影响较大,多数区域垂直方向年形变速率大于10 mm/a;通过本文方法对研究区潜在滑坡进行识别,发现在研究区的21处历史滑坡点中,有16处被识别为潜在滑坡,5处未被识别但也位于发生形变的区域内,表明本文方法对潜在滑坡的识别精度高,具有可行性。该研究为识别采空附近的潜在滑坡提供了一种新的思路,可以有效识别采空区附近山体边坡是否处于潜在的、不明显的滑动状态,对滑坡灾害具有预警作用。     

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.     

Centrifuge model test of an irrigation-induced loess landslide in the Heifangtai loess platform,Northwest China

The Heifangtai platform in Northwest China is famous for irrigation-induced loess landslides. This study conducted a centrifuge model test with reference to an irrigation-induced loess landslide that occurred in Heifangtai in 2011. The loess slope model was constructed by whittling a cubic loess block obtaining from the landslide site. The irrigation water was simulated by applying continuous infiltration from back of the slope. The deformation, earth pressure, and pore pressure were investigated during test by a series of transducers. For this particular study, the results showed that the failure processes were characterized by retrogressive landslides and cracks. The time dependent reductions of cohesion and internal friction angle at basal layer with increasing pore-water pressure were responsible for these failures. The foot part of slope is very important for slope instability and hazard prevention in the study area, where concentration of earth pressure and generation of high pore-water pressures would form before failures. The measurements of earth pressure and pore-water pressure might be effective for early warning in the study area.     

三峡库区麻柳林滑坡变形特征及演化模拟

在库水位波动和降雨作用的共同影响下，库岸滑坡的变形规律往往更为复杂。以三峡库区麻柳林滑坡为例，基于野外调查、钻探编录、深部位移监测以及数值模拟等手段，分析了库水位波动和降雨作用下滑坡变形特征及演化规律。结果表明:麻柳林滑坡在粉质黏土层和块石层交界处发育一个次级滑带，目前该滑坡主要沿次级滑带运动，导致次级滑动的原因与坡体物质的差异性有关；S_i(S_f)指标分析法揭示滑坡的滑带还未完全破坏，滑坡仍处于蠕变状态；根据三峡水库水位调度规律，将一个完整水文年划分为6个阶段，数值模拟结果表明滑坡在库水位缓慢下降阶段变形速率较小、在快速下降阶段和低水位阶段变形速率持续增大、在快速上升阶段和缓慢上升阶段以及高水位阶段变形速率则保持平稳。其中，降雨的直接影响和降雨导致库水位波动进而对滑坡变形造成的间接影响，使得麻柳林滑坡在低水位阶段的变形显著增加、稳定性最差，应加强该时段内滑坡的监测和预警。