Modeling of rainfall-triggered shallow landslide

By integrating hydrological modeling with the infinite slope stability analysis, a rainfall-triggered shallow landslide model was developed by Iverson (Water Resour Res 36:1897-1910, 2000). In Iverson’s model, the infiltration capacity is assumed to be equivalent to the saturated hydraulic conductivity for finding pressure heads analytically. However, for general infiltration process, the infiltration capacity should vary with time during the period of rain, and the infiltration rate is significantly related to the variable infiltration capacity. To avoid the unrealistically high pressure heads, Iverson employed the beta-line correction by specifying that the simulated pressure heads cannot exceed those given by the beta line. In this study, the suitability of constant infiltration capacity together with the beta-line correction for hydrological modeling and landslide modeling of hillslope subjected to a rainfall is examined. By amending the boundary condition at ground surface of hillslope in Iverson’s model, the modified Iverson’s model with considering general infiltration process is developed to conduct this examination. The results show that the unrealistically high pressure heads from Iverson’s model occur due to the overestimation of infiltration rate induced from the assumption that the infiltration capacity is identical to the saturated hydraulic conductivity. Considering with the general infiltration process, the modified Iverson’s model gives acceptable results. In addition, even though the beta-line correction is applied, the Iverson’s model still produces greater simulated pressure heads and overestimates soil failure potential as compared with the modified Iverson’s model. Therefore, for assessing rainfall-triggered shallow landslide, the use of constant infiltration capacity together with the beta-line correction needs to be replaced by the consideration of general infiltration process.     

Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey

In the last decades, landslide hazard assessment has attracted many researchers' attention. A number of parameters are suggested to be responsible to quantitatively explain the mechanism of landslides; many of these parameters are very important and factual. However, some data types and models are site-specific and could not be applied to different locations. Furthermore, the data stored in continuous parameter maps are divided into a number of classes arbitrarily, depending on the vision of the expert. Basically, this division controls the result of bivariate analysis. Besides, the responsible portion of the parameter map controlling the mechanism is also weighted arbitrarily. Based on these two facts, the class boundaries put a prejudice on the produced susceptibility/hazard maps, which result in dependence on the knowledge of the user rather than being dependent on the data and the fact itself. The aim of this study is to refine the previously defined methods in a more data-dependent trend. To achieve this goal, two new concepts: seed cells and percentile maps are introduced. Seed cells are the zones that are considered to represent the best undisturbed morphological decision rules (conditions before landslide occurs) and would be achieved by adding a buffer zone to the crown and flank areas of the landslide. To quantitatively classify the input parameter maps, the data distributions of seed cells in the parameter maps are divided into a number of classes on the basis of their distribution's percentile break-points upon which the parameter maps are directly dependent on the seed cell distributions, hence to the data itself.     

Seismically induced landslide displacements: a predictive model

Newmark's model for predicting earthquake-induced landslide displacements provides a simple way to predict the coseismic displacements affecting a sliding mass subject to earthquake loading. In this model, seismic slope stability is measured in terms of critical acceleration, which depends on the mechanical soil properties, pore-pressure distribution, and slope geometry. The triggering seismic forces are investigated in terms of energy radiation from the source, propagation, and site effects, based on 190 accelerometric recordings from 17 Italian earthquakes with magnitudes between 4.5 and 6.8. The method is based on the calibration of relations having the general form of an attenuation law that relates the energy of the seismic forces to the dynamic shear resistances of the sliding mass to propagate the expected landslide displacements as an inverse function of the distance from the fault rupture; the amount of displacement computed through these relations provides a criterion to predict the occurrence of slope failures. Finally, maps showing, in a deterministic and a probabilistic way, the potential of seismically induced landslide displacements are displayed as a tool to provide seismic landslide scenarios and earthquake-induced landslide hazard maps, respectively.     

Structural geomorphology,active faulting and slope deformations in the epicentre area of the MW 7.0, 1857, Southern Italy earthquake

In tectonically active domains, fault propagation processes are revealed by landforms. A characteristic morphological feature of exposed active faults is the occurrence of triangular facets. Triangle-shaped landforms morphologically similar to fault-related triangular facets result from erosion of moderately-to-steeply dipping strata or layered successions along fold limbs and are known as flatirons. Triangular facets and flatirons may commonly coexist in mountain chains resulting from the superposition of recent extensional faulting on inherited fold-and-thrust architecture. In these settings analysis of flatirons and triangular facets is not trivial and may result in geomorphologic misinterpretations, hence undermine the interpretations of the geological structure and related deformation history. Here we show that active fault lineaments can be discriminate by inherited morpho-structure. We present the combined results of photo-geological and field survey carried out along well-exposed triangular shaped landforms located in the seismically active Upper Agri River Valley of the Southern Apennine. We found that triangular shaped landforms develop along a major fold back-limb, the Mt. Lama western slope, making it possible to unequivocally interpret these as flatirons. Downslope, Mt. Lama back limb is affected by a large deep-sited landslide whose scarp bound the flatirons and mimics a Holocene fault scarp. This inference appears significant, since the investigated landforms were interpreted by many authors as triangular facets related to an active normal fault, located at its foot, responsible for large historical earthquakes (i.e., the 1857 earthquake). We provide new criteria to discriminate morphologic convergence in tectonically active domains, with consequences for the regional geological interpretation, and for the assessment of geological hazards.     

Seismically reactivated Hattian slide in Kashmir, Northern Pakistan

The Pakistan 2005 earthquake, of magnitude 7.6, caused severe damage on landscape and infrastructure, in addition to numerous casualties. The event reactivated Hattian Slide, creating a rock avalanche in a location where earlier mass movements had happened already, as indicated by satellite imagery and ground investigation. The slide originated on Dana Hill, in the upper catchment area of Hattian on Karli Stream, a tributary of Jhelum River, Pakistan, and buried the hamlet Dandbeh and several farms nearby. A natural dam accumulated, impounding two lakes, the larger one threatening parts of downstream Hattian Village with flooding. An access road and artificial spillways needed to be constructed in very short time to minimize the flooding risk. As shown by this example, when pointing out the risk of large-scale damage to population and infrastructure by way of hazard indication maps of seismically active regions, and preparing for alleviation of that risk, it is advisable to consider the complete Holocene history of the slopes involved.     

历史地震与滑坡灾害

通过对我国中强地震诱发滑坡或产生次生灾害的实例的研究，认为，地震作用是诱发滑坡灾害的重要因素之一。提出加强中强地震史料的发掘和研究，进行地震诱发滑坡、古滑坡或古地震的考察，是历史地震工作者在新形势下服务于国民经济建设、拓宽社会服务领域的重要方向之一。     

联合DBSCAN聚类采样和SVM分类的滑坡易发性评价

针对基于机器学习的滑坡易发性评价中非滑坡样本选取不规范导致的分类精度较低问题,本文提出联合基于密度的噪声应用空间聚类（Density-Based Spatial Clustering of Applications with Noise,DBSCAN）采样策略和支持向量机（Support Vector Machine,SVM）分类方法的DBSCAN-SVM滑坡易发性评价模型。首先,基于DBSCAN聚类和空间分析选取非滑坡样本;然后,将样本数据代入SVM分类模型进行训练与验证,预测并提取SVM分类中属于滑坡的概率,获得滑坡易发性;最后,以四川省绵阳市为试验区,预测滑坡易发性概率,基于滑坡易发性精度与分级结果等要素,与传统非滑坡样本采集策略的SVM滑坡易发性评价模型进行对比,并结合实际情况对DBSCAN-SVM模型评价结果进行分析。研究结果表明,相比传统SVM滑坡易发性评价模型,本文提出的DBSCAN-SVM滑坡易发性评价模型在高易发区和极高易发区中包含的滑坡样本数量较多,准确率、召回率、AUC、F1分数均得到提高,精度较高。     

Landslide intelligent prediction using object-oriented method

Landslides are one of the most serious geological disasters in the world and happen quite frequently in the Three Gorges. Landslide prediction is a very important measure of landslide prevention and cure in the Three Gorges. Traditional methods lack in sufficiently mining the various complex information from a landslide system. They often need much manual intervention and possess poor intelligence and accuracy. An intelligent method proposed in this paper for landslide prediction based on an object-oriented method and knowledge driving is hopeful to solve the above problem. The method adopted Landsat ETM+ images, 1:50,000 geological map and 1:10,000 relief map in the Three Gorges as the data origins. It firstly produced the key factors influencing landslide development and used multi-resolution segmentation algorithm to segment the image objects based on the key landslide factors of engineering rock group, reservoir water fluctuation, slope structure and slope level. Secondly, the method chose some sample objects and adopted the decision tree algorithm C5.0 to mine the landslide forecast criteria according to the factor values of each sample object. Finally, under knowledge driving the method classified the image objects and realized landslide susceptibility analysis and intelligent prediction in the Three Gorges. The method proposed in this paper is object-oriented. Results of a real-world example show that: (1) the object-oriented method possesses much more compact knowledge representation, higher efficiency, more continuous classifying result and higher prediction accuracy compared with the pixel-oriented method; (2) it possesses the overall accuracy of 87.64% and kappa coefficient of 0.8305 and is more accurate than the other seven methods (such as the pixel-oriented methods of Parallelpiped, Minimum Distance, Maximum Likelihood, Mahalanobis Distance, K-means and Isodata and the object-oriented method of Nearest Neighbor); (3) about 46.97% landslides lie in the high susceptibility region, 24.24% landslides lie in the moderate susceptibility region, 27.27% landslides lie in the low susceptibility region and 1.52% landslides lie in the very low susceptibility region. Therefore the method can effectively realize landslide susceptibility analysis and provides a new idea for landslide intelligent and accurate prediction.     

Hawaii bibliographic database

 The Hawaii bibliographic database has been created to contain all of the literature, from 1779 to the present, pertinent to the volcanological history of the Hawaiian-Emperor volcanic chain. References are entered in a PC- and Macintosh-compatible EndNote Plus bibliographic database with keywords and abstracts or (if no abstract) with annotations as to content. Keywords emphasize location, discipline, process, identification of new chemical data or age determinations, and type of publication. The database is updated approximately three times a year and is available to upload from an ftp site. The bibliography contained 8460 references at the time this paper was submitted for publication. Use of the database greatly enhances the power and completeness of library searches for anyone interested in Hawaiian volcanism. Received: 1 June 1997 / Accepted: 17 September 1997     

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.