滑坡定量预测的非线性理论方法

滑坡时空预测是当前滑坡研究中的难题，特别是滑坡时间预测工作，其进展缓慢。本文基于非线性科学理论，分析了滑坡活动的分形特征及时间分形预测方法，研究了滑坡空间预测的人工神经网络特征，系统介绍了滑坡时间预测的非线性动力学理论。在此基础上，讨论了滑坡定量预测的发展趋势。     

杭州市滑坡地质灾害危险性区划与评价

基于证据权法构建滑坡地质灾害评价模型,进行杭州市滑坡地质灾害危险性区划研究。主要数据源包括1930－2009年杭州市域采集到的1 905个地质灾害个例以及杭州市地质图、土地利用数据及数字高程模型（DEM）等。利用Arcgis空间分析及信息提取功能,筛选强降水、地层岩性、坡度、坡向、坡高、河网与道路缓冲等证据因子,并运用证据权法客观确定各因子权重, 最后通过Arc-WofE扩展模块对多种优选因子的叠加,计算任意格网单元的滑坡发生概率,实现对潜在滑坡点位的空间预测。经分离样本法验证,区划准确率为88.3%,分析结果与现有滑坡的分布情况比较吻合。据此表明证据权法在多指标评价及其权重确定等方面具有普适性,值得在滑坡地质灾害危险性区划等方面推广应用。     

Palaeochannel-controlled depositional features of the cretaceous lower Mahadek uranium Mineralisation in Umthongkut area,West Khasi Hills,Meghalaya

GIS complemented statistical classification techniques yield good result in predicting landslide hazards. Indian standard landslide hazard model follows guidelines formulated by the Bureau of Indian Standards (BIS, 1998), in which the study area is divided into five categories, ranging from very low hazard zone to very high hazard zone on fixed numerical ratings. For land use planners, “moderate hazard zone” proves vague and indecisive. In the present study, BIS based landslide hazard zones are demarcated for 140 sq. km area for a road corridor in East and North Sikkim that shows 21.96%, 53.14%, 22.80% and 2.10% for ‘Low Hazard Zone’, ‘Moderate Hazard Zone’, ‘High Hazard Zone’ and ‘Very High Hazard Zone’ respectively. This classification scheme has been reclassified to binary system based on population distribution and defining the cut-off by evaluation techniques of the ROC. The reclassification eliminates “moderate hazard zone”, minimizing the Type-II error and becomes more acceptable for future land use planning.     

Evaluation and validation of landslide spatial susceptibility in the Western Ghats of Kerala, through GIS-based Weights of Evidence model and Area Under Curve technique

Landslide susceptibility mapping and spatial prediction have been carried out for the headwater region of Manimala river basin in the Western Ghats of Kerala, India, through geographic information technology and bayesian statistics, Weights of Evidence (WofE) model. The variables such as geomorphology, slope, relative relief, terrain curvature, slope length and steepness, soil type and land use/land cover are considered as factors that translate the terrain susceptible to landsliding. The quantitative relationship between landslides and the causative factors were statistically weighted using the ArcSDM extension of ArcGIS software. The posterior probability map, produced on the basis of predictive weights for each variable by combining the weighted layers in GIS, shows a high posterior probability value of 0.1 (highly possible) with a standard deviation of 0.0025. The discrete susceptibility classes in the reclassified posterior probability map reveals that the high and moderate landslide susceptibility classes cover 0.78 and 14.93% respectively of the total study area. The result was validated using the Area Under Curve (AUC) method with a separate set of landslide locations and the validation demonstrates high prediction accuracy with a prediction rate of 81.32%.     

Application and verification of a fractal approach to landslide susceptibility mapping

Landslide susceptibility mapping is essential for land-use activities and management decision making in hilly or mountainous regions. The existing approaches to landslide susceptibility zoning and mapping require many different types of data. In this study, we propose a fractal method to map landslide susceptibility using historical landslide inventories only. The spatial distribution of landslides is generally not uniform, but instead clustered at many different scales. In the method, we measure the degree of spatial clustering of existing landslides in a region using a box-counting method and apply the derived fractal clustering relation to produce a landslide susceptibility map by means of GIS-supported spatial analysis. The method is illustrated by two examples at different regional scales using the landslides inventory data from Zhejiang Province, China, where the landslides are mainly triggered by rainfall. In the illustrative examples, the landslides from the inventory are divided into two time periods: The landslides in the first period are used to produce a landslide susceptibility map, and those in the late period are taken as validation samples for examining the predictive capability of the landslide susceptibility maps. These examples demonstrate that the landslide susceptibility map created by the proposed technique is reliable.     

A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas

Landslides are one of the most destructive phenomena of nature that cause damage to both property and life every year, and therefore, landslide susceptibility zonation (LSZ) is necessary for planning future developmental activities. In this paper, apart from conventional weighting system, objective weight assignment procedures based on techniques such as artificial neural network (ANN), fuzzy set theory and combined neural and fuzzy set theory have been assessed for preparation of LSZ maps in a part of the Darjeeling Himalayas. Relevant thematic layers pertaining to the causative factors have been generated using remote sensing data, field surveys and Geographic Information System (GIS) tools. In conventional weighting system, weights and ratings to the causative factors and their categories are assigned based on the experience and knowledge of experts about the subject and the study area to prepare the LSZ map (designated here as Map I). In the context of objective weight assignments, initially the ANN as the black box approach has been used to directly produce an LSZ map (Map II). In this approach, however, the weights assigned are hidden to the analyst. Next, the fuzzy set theory has then been implemented to determine the membership values for each category of the thematic layer using the cosine amplitude method (similarity method). These memberships are used as ratings for each category of the thematic layer. Assuming weights of each thematic layer as one (or constant), these ratings of the categories are used for the generation of another LSZ map (Map III). Subsequently, a novel weight assignment procedure based on ANN is implemented to assign the weights to each thematic layer objectively. Finally, weights of each thematic layer are combined with fuzzy set derived ratings to produce another LSZ map (Map IV). The maps I–IV have been evaluated statistically based on field data of existing landslides. Amongst all the procedures, the LSZ map based on combined neural and fuzzy weighting (i.e., Map IV) has been found to be significantly better than others, as in this case only 2.3% of the total area is found to be categorized as very high susceptibility zone and contains 30.1% of the existing landslide area.     

Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management.     

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.     

Combining neural network with fuzzy,certainty factor and likelihood ratio concepts for spatial prediction of landslides

The landslide studies can be categorized as pre- and postdisaster studies. The predisaster studies include spatial prediction of potential landslide zones known as landslide susceptibility zonation (LSZ) mapping to identify the areas/locales susceptible to landslide hazard. The LSZ maps provide an assessment of the safety of existing habitations and infrastructural/functional elements and help plan further developmental activities in the hilly regions. Landslides are one of the natural geohazards that affect at least 15% of land area of India. Different types of landslides occur frequently in geodynamical active domains of the Himalayas. In India, various techniques have been developed and adopted for LSZ mapping of different regions. However, the technique for LSZ mapping is not yet standardized. The present research is an attempt in this direction only. In our earlier work (Kanungo et al. 2006), a detailed study on conventional, artificial neural network (ANN)- black box-, fuzzy set-based and combined neural and fuzzy weighting techniques for LSZ mapping in Darjeeling Himalayas has been documented. In this paper, other techniques such as combined neural and certainty factor concept along with combined neural and likelihood ratio techniques have been assessed in comparison with combined neural and fuzzy technique for the preparation of LSZ maps of the same study area in parts of Darjeeling Himalayas. It is observed from the present study that the LSZ map produced using combined neural and fuzzy approach appears to be the most accurate one as in this case only 2.3% of the total area is found to be categorized as very high susceptibility zone and contains 30.1% of the existing landslide area. This approach can serve as one of the key objective approaches for spatial prediction of landslide hazards in hilly terrain.     

GIS-based assessment of landslide susceptibility on the base of the Weights-of-Evidence model

The major scope of the study is the assessment of landslide susceptibility of Flysch areas including the Penninic Klippen in the Vienna Forest (Lower Austria) by means of Geographical Information System (GIS)-based modelling. A statistical/probabilistic method, referred to as Weights-of-Evidence (WofE), is applied in a GIS environment in order to derive quantitative spatial information on the predisposition to landslides. While previous research in this area concentrated on local geomorphological, pedological and slope stability analyses, the present study is carried out at a regional level. The results of the modelling emphasise the relevance of clay shale zones within the Flysch formations for the occurrence of landslides. Moreover, the distribution of mass movements is closely connected to the fault system and nappe boundaries. An increased frequency of landslides is observed in the proximity to drainage lines, which can change to torrential conditions after heavy rainfall. Furthermore, landslide susceptibility is enhanced on N-W facing slopes, which are exposed to the prevailing direction of wind and rainfall. Both of the latter geofactors indirectly show the major importance of the hydrological conditions, in particular, of precipitation and surface runoff, for the occurrence of mass movements in the study area. Model performance was checked with an independent validation set of landslides, which are not used in the model. An area of 15% of the susceptibility map, classified as highly susceptible, “predicted” 40% of the landslides.     

Interval prediction of landslide displacement with dual-output least squares support vector machine and particle swarm optimization algorithms

For landslide displacement, interval predictions are generally more realistic and reliable compared with traditional point predictions. This paper presents a new interval prediction method for landslide displacement integrating dual-output least squares support vector machine (DO-LSSVM) and particle swarm optimization (PSO) algorithms. In this new method, the PSO algorithm is employed to optimize coefficients of the least squares support vector machine (LSSVM) model for obtaining point prediction results, and the interval prediction of the landslide displacement is made based on the dual-outputs obtained from the DO-LSSVM model. To assess the rationality of the predictions, three performance evaluation indicators, including the prediction interval coverage probability (PICP), normalized mean prediction interval width (NMPIW), and coverage width-based criterion (CWC), are established. Case studies of the Tanjiahe landslide and the Baishuihe landslide in the Three Gorges Reservoir region are then used to demonstrate the effectiveness of the proposed method in predicting the landslide displacement interval. The case study results demonstrate that this new method has the best overall performance compared with other existing methods, and this new method can provide accurate and reliable results for the medium- to long-term interval prediction of landslide displacement.

     

滑坡运动路径复杂度研究:综述与展望

滑坡运动路径具有普遍的复杂性,体现为侧散、转向、分叉、交织、聚合与并联等复杂行为.滑坡运动路径复杂性增大了滑坡危险性.因而,滑坡危险性评估对滑坡运动路径复杂度的量化和概率分布研究提出了需求.系统地梳理和总结了滑坡运动路径复杂度的研究现状,指出了相关研究所面临的关键问题,并进行了未来研究展望.总体上,当前关于滑坡运动路径复杂行为的研究,主要面临量化研究稀缺、概率分布研究欠缺的问题.具体表现在:现有滑坡运动路径的剖面线概化方法难以处理多路径复杂行为;现有零星的指标不能满足滑坡运动路径复杂度的系统性科学量化;滑坡运动路径复杂度概率分布的分布函数不明、主控因素不清.进一步,针对待解决的关键问题,本文在研究展望中提出:主要通过分段单路径化方案,实现滑坡运动多路径复杂行为的剖面线概化;构建基于剖面线的滑坡运动路径复杂度的量化指标体系,突破量化难题;基于大量滑坡实例数据,确定滑坡运动路径复杂度概率分布的分布函数、查清其主控因素;最终,实现滑坡运动路径复杂度概率分布的预测建模,支撑滑坡危险性及风险定量评估.      

降雨型滑坡时间概率的逻辑回归拟合及连续概率滑坡危险性建模

提高降雨型滑坡危险性预警精度和空间辨识度具有重要意义.以江西宁都县1980—2001年156个降雨型滑坡为例,首先基于传统的EE-D(early effective rainfall-rainfall duration)阈值法计算不同降雨诱发滑坡的时间概率级别;然后以各级别临界降雨阈值曲线对应的时间概率为因变量,并以对应的前期有效降雨量(early effective rainfall,EE)和降雨历时(D)为自变量,采用逻辑回归拟合出上述因变量与自变量之间的非线性关系,得到降雨诱发滑坡的连续概率值;之后对比C5.0决策树和多层感知器的滑坡易发性预测性能;最后利用降雨诱发滑坡的连续概率值与易发性图相耦合以实现连续概率滑坡危险性预警.结果显示：(1)宁都降雨型滑坡连续概率值的逻辑回归方程为1/P=1+e^{4.062+0.747 4×D-0.079 44×EE},其拟合优度为0.983;(2)2002—2003年的20处用于连续概率阈值测试的降雨型滑坡大都落在连续概率值大于0.7的区域,只有4处落在小于0.7的区域;(3)C5.0决策树预测滑坡易发性的精度显著高于多层感知...     

Integrating spatial, temporal, and magnitude probabilities for medium-scale landslide risk analysis in Darjeeling Himalayas, India

Landslide risk assessment is based on spatially integrating landslide hazard with exposed elements-at-risk to determine their vulnerability and to express the expected direct and indirect losses. There are three components that are relevant for expressing landslide hazard: spatial, temporal, and magnitude probabilities. At a medium-scale analysis, this is often done by first deriving a landslide susceptibility map, and to determine the three types of probabilities on the basis of landslide inventories linked to particular triggering events. The determination of spatial, temporal, and magnitude probabilities depend mainly on the availability of sufficiently complete historical records of past landslides, which in general are rare in most countries (e.g., India, etc.). In this paper, we presented an approach to use available historical information on landslide inventories for landslide hazard and risk analysis on a medium scale (1:25,000) in a perennially typical data-scarce environment in Darjeeling Himalayas (India). We demonstrate how the incompleteness in the resulting landslide database influences the various components in the calculation of specific risk of elements-at-risk (e.g., buildings, population, roads, etc.). We incorporate the uncertainties involved in the risk estimation and illustrate the range of expected losses in the form of maximum and minimum loss curves. The study demonstrates that even in data-scarce environments, quantitative landslide risk assessment is a viable option, as long as the uncertainties involved are expressed.     

湖北省巴东县新城区滑坡灾害空间预测

论文在综合分析巴东县新城区区域地质背景及滑坡基本特征的基础上,分析了影响滑坡灾害发生的各因素与滑坡发生之间的相关性,提出影响灾害发生的主要因素：地形坡度、斜坡形态、岩性、构造、水的作用、人类工程活动。利用GIS的空间分析功能将各因素图件栅格化为86216个不规则单元。基于逻辑回归的方法对滑坡灾害进行了定量的概率预测。同时,对研究区进行滑坡灾害空间预测分区,将预测结果与该地区历史滑坡灾害发生情况进行对比发现预测精度为85．71％。说明所建立的模型具有较高的预测精度,是可以用于预测分析的。     

数字滑坡技术及其应用

“数字滑坡”技术,就是以遥感（RS）和空间定位方法为主,结合其他勘探、试验、调查手段获取数字形式的、与地理坐标配准的滑坡基本信息;并利用GIS技术存贮和管理这些数字信息;在此基础上,根据滑坡地学原理进行空间分析,研制各类模型,并服务于滑坡调查、监测、研究、滑坡灾害评价、危险预测、灾情评估、滑坡防治等。通过金龙山三维数字模型,卫星监测易贡滑坡、三峡库区重点城镇滑坡及千将坪滑坡等地的遥感调查说明数字滑坡技术的实际应用。     

Method for prediction of landslide movements based on random forests

Prediction of landslide movements with practical application for landslide risk mitigation is a challenge for scientists. This study presents a methodology for prediction of landslide movements using random forests, a machine learning algorithm based on regression trees. The prediction method was established based on a time series consisting of 2 years of data on landslide movement, groundwater level, and precipitation gathered from the Kostanjek landslide monitoring system and nearby meteorological stations in Zagreb (Croatia). Because of complex relations between precipitations and groundwater levels, the process of landslide movement prediction is divided into two separate models: (1) model for prediction of groundwater levels from precipitation data and (2) model for prediction of landslide movements from groundwater level data. In a groundwater level prediction model, 75 parameters were used as predictors, calculated from precipitation and evapotranspiration data. In the landslide movement prediction model, 10 parameters calculated from groundwater level data were used as predictors. Model validation was performed through the prediction of groundwater levels and prediction of landslide movements for the periods from 10 to 90 days. The validation results show the capability of the model to predict the evolution of daily displacements, from predicted variations of groundwater levels, for the period up to 30 days. Practical contributions of the developed method include the possibility of automated predictions, updated and improved on a daily basis, which would be an important source of information for decisions related to crisis management in the case of risky landslide movements.     

基于时间序列与人工蜂群支持向量机的滑坡位移预测研究

总结以往滑坡预测方法存在的诸多不足,针对滑坡监测位移-时间曲线特点,本文提出了一种基于时间序列的人工蜂群算法（ABC）与支持向量回归机（SVR）相结合的滑坡位移预测方法。以三峡库区白水河滑坡为例,通过对滑坡位移、降雨、库水位等因素的分析,研究影响滑坡位移变化的因素。用时间序列加法模型和移动平均法将滑坡位移分解为趋势项和周期项。以多项式最小二乘法拟合滑坡位移趋势项,用人工蜂群支持向量机模型对滑坡位移周期项进行训练和预测。通过灰色系统关联分析法计算多项因子与滑坡位移周期项之间的关联性。最终的滑坡总位移预测值为周期项预测值与趋势项预测值之和。与BP神经网络、PSO-SVR模型方法相比,该方法在滑坡位移预测中有更高的精度,在防灾减灾工作中有较好的推广应用前景。     

A fuzzy set based approach for integration of thematic maps for landslide susceptibility zonation

Spatial prediction of landslides is termed landslide susceptibility zonation (LSZ). In this study, an objective weighting approach based on fuzzy concepts is used for LSZ in a part of the Darjeeling Himalayas. Relevant thematic layers pertaining to landslide causative factors have been generated using remote sensing and geographic information system (GIS) techniques. The membership values for each category of thematic layers have been determined using the cosine amplitude fuzzy similarity method and are used as ratings. The integration of these ratings led to the generation of LSZ map. The integration of different ratings to generate an LSZ map has been performed using a fuzzy gamma operator apart from the arithmetic overlay approach. The process is based on determination of combined rating known as the landslide susceptibility index (LSI) for all the pixels using the fuzzy gamma operator and classification using the success rate curve method to prepare the LSZ map. The results indicate that as the gamma value increases, the accuracy of the LSZ map also increases. It is observed that the LSZ map produced by the fuzzy algebraic sum has reflected a more real situation in terms of landslides in the study area.     

滑坡风险评估的难点和进展

近年来,国内外滑坡研究日益重视滑坡风险评估和管理技术方法的研究,但滑坡风险评估依然是存在很多问题和难点,尤其是在中等—大比例尺区域滑坡风险定量评估方面,主要表现在滑坡编录数据库建设、滑坡影响因素的识别和建模、滑坡时间、空间预测的不确定性,滑坡诱发因素动态变化的定量刻画,承灾体识别和易损性定量评价等方面。在阐述滑坡风险评估流程的基础上,围绕滑坡风险评估与制图中滑坡编录和基础数据获取与更新,危险性分析中的滑坡空间、时间概率和滑坡特征预测、损失评估中的易损性分析与定量和承灾体定量化制图等技术方法中的难点和存在的问题,概述针对这些问题所取得的研究进展,并指出了滑坡风险研究的技术发展趋势。