APPLICATION OF LOGISTIC REGRESSION MODEL FOR HAZARD ASSESSMENT OF EARTHQUAKE-TRIGGERED LANDSLIDES: A CASE STUDY OF 2017 JIUZHAIGOU(CHINA)MS7.0 EVENT

The M_S7.0 Jiuzhaigou earthquake in Sichuan Province of 8 August 2017 triggered a large number of landslides. A comprehensive and objective panorama of these landslides is of great significance for understanding the mechanism, intensity, spatial pattern and law of these coseismic landslides, recovery and reconstruction of earthquake affected area, as well as prevention and mitigation of landslide hazard. The main aim of this paper is to present the use of remote sensing images, GIS technology and Logistic Regression(LR)model for earthquake triggered landslide hazard mapping related to the 2017 Jiuzhaigou earthquake. On the basis of a scene post-earthquake Geoeye-1 satellite image(0.5m resolution), we delineated 4834 co-seismic landslides with an area of 9.63km². The ten factors were selected as the influencing factors for earthquake triggered landslide hazard mapping of Jiuzhaigou earthquake, including elevation, slope angle, aspect, horizontal distance to fault, vertical distance to fault, distance to epicenter, distance to roads, distance to rivers, TPI index, and lithology. Both landsliding and non-landsliding samples were needed for LR model. Centroids of the 4834 initial landslide polygons were extracted for landslide samples and the 4832 non-landslide points were randomly selected from the landslide-free area. All samples(4834 landslide sites and 4832 non-landslide sites)were randomly divided into the training set(6767 samples)and validation set(2899 samples). The logistic regression model was used to carry out the landslide hazard assessment of the Jiuzhaigou earthquake and the results show that the landslide hazard assessment map based on LR model is very consistent with the actual landslide distribution. The areas of Wuhuahai-Xiamo, Huohuahai and Inter Continental Hotel of Jiuzhai-Ruyiba are high hazard areas. In order to quantitatively evaluate the prediction results, the trained model calculated with the training set was evaluated by training set and validation set as the input of the model to get the output results of the two sets. The ROC curve was used to evaluate the accuracy of the model. The ROC curve for LR model was drawn and the AUC values were calculated. The evaluation result shows good prediction accuracy. The AUC values for the training and validation data set are 0.91 and 0.89, respectively. On the whole, more than 78.5% of the landslides in the study area are concentrated in the high and extremely high hazard zones. Landslide point density and landslide area density increase very rapidly as the level of hazard increases. This paper provides a scientific reference for earthquake landslides, disaster prevention and mitigation in the earthquake area.     

基于回声探测监测沉水植物盖度的插值方法研究:以杭州西湖沉水植物盖度监测为例

沉水植物作为水生态系统的重要组成成分,在水生态系统物质循环和能量流动中发挥着重要作用,其覆盖度和生物量是评价湖泊等浅水水体系统稳定性的关键参数随着高效和无损伤监测的回声探测仪在沉水植物盖度监测中的应用,其精确度算法也受到了越来越多的关注本研究以成功恢复沉水植物的浅水湖泊杭州西湖为研究对象,利用BioSonics便携型回声探测仪——MX采集沉水植物回声样本同时结合人工样方设置,采集与回声探测对应位点的沉水植物样本,验证回声探测结果的精确性通过建立回归模型分析回声探测得到的沉水植物体积百分比(PVI)与人工样方获得的对应平均鲜重关系,结果表明二者具有较好的相关性分别采用普通克里金法、反距离权重法、径向基函数法3种插值方法对同一季节的不同湖泊和同一湖泊的不同季节未采集区域沉水植物的盖度数据进行插值分析,并对插值结果进行交叉验证,以确定方法的精确度交叉验证结果表明,插值精确度反距离权重法径向基函数法普通克里金法研究结果为回声探测与插值分析方法结合在大尺度浅水水体中沉水植物监测应用提供了技术支撑.     

Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers

Landslides are considered as one among many phenomena jeopardizing human beings as well as their constructions. To prevent this disastrous problem, researchers have used several approaches for landslide susceptibility modeling, for the purpose of preparing accurate maps marking landslide prone areas. Among the most frequently used approaches for landslide susceptibility mapping is the Artificial Neural Network (ANN) method. However, the effectiveness of ANN methods could be enhanced by using hybrid metaheuristic algorithms, which are scarcely applied in landslide mapping. In the current study, nine hybrid metaheuristic algorithms, genetic algorithm (GA)-ANN, evolutionary strategy (ES)-ANN, ant colony optimization (ACO)-ANN, particle swarm optimization (PSO)-ANN, biogeography based optimization (BBO)-ANN, gravitational search algorithm (GHA)-ANN, particle swarm optimization and gravitational search algorithm (PSOGSA)-ANN, grey wolves optimization (GWO)-ANN, and probability based incremental learning (PBIL)-ANN have been used to spatially predict landslide susceptibility in Algiers’ Sahel, Algeria. The modeling phase was done using a database of 78 landslides collected utilizing Google Earth images, field surveys, and six conditioning factors (lithology, elevation, slope, land cover, distance to stream, and distance to road). Initially, a gamma test was used to decrease the input variable numbers. Furthermore, the optimal inputs have been modeled by the mean of hybrid metaheuristic ANN techniques and their performance was assessed through seven statistical indicators. The comparative study proves the effectiveness of the co-evolutionary PSOGSA-ANN model, which yielded higher performance in predicting landslide susceptibility compared to the other models. Sensitivity analysis using the step-by-step technique was done afterward, which revealed that the distance to the stream is the most influential factor on landslide susceptibility, followed by the slope factor which ranked second. Lithology and the distance to road have demonstrated a moderate effect on landslide susceptibility. Based on these findings, an accurate map has been designed to help land-use managers and decision-makers to mitigate landslide hazards.     

Application of the radial basis function neural network to the short term prediction of the Earth’s polar motion

By a number of test cases using different sample numbers and sample lengths, we obtain a Radial Basis Function Neural Network (RBFNN) model that is suitable for the short-term forecast of polar motion, especially for the ultra-short-term forecast. By using the same data sample of Earth’s polar motion, this RBFNN model can achieve better short-term prediction accuracy than the least-squares+autoregressive (LS+AR) method, and better ultra-short-term prediction accuracy than the LS+AR+Kalman method. Using this model to forecast the polar motion data from January 1, 2002 to December 30, 2007 and from January 1, 2010 to December 30, 2016, respectively, experimental results show that the ultra-short-term forecast accuracy of this RBFNN model is within a precision of 3.15 and 3.08 milliseconds of arc (mas) in polar motion x direction, 2.02 and 2.04 mas in polar motion y direction; the short-term forecast accuracy of RBFNN model is within a precision of 8.83 and 8.69 mas in polar motion x direction, and 5.59 and 5.85 mas in polar motion y direction. As is stated above, this RBFNN model is well capable of forecasting the short-term of polar motion, especially the ultra-short-term.     

基于证据权方法的玉树地震滑坡危险性评价

玉树地震诱发了2 036处滑坡。应用地理信息系统与遥感技术,选取与地表破裂距离、峰值加速度(PGA)、高程、坡度、坡向、曲率、坡位、与水系距离、岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)等12个因素作为玉树地震滑坡危险性评价因子,采用加法与减法2种证据权方法,开展玉树地震滑坡危险性评价研究工作。结果表明:基于加法证据权方法得到评价结果的正确率为80.32%,基于减法证据权方法得到结果的正确率为80.19%。将滑坡危险性评价结果图分为极高危险区、高危险区、中危险区、低危险区与极低危险区5类。这一成果可划分出滑坡危险区,为灾后滑坡防治、基础设施重建与自然环境保护提供参考。     

Radial basis function neural network for hydrologic inversion: an appraisal with classical and spatio-temporal geostatistical techniques in the context of site characterization

This paper investigates three techniques for spatial mapping and the consequential hydrologic inversion, using hydraulic conductivity (or transmissivity) and hydraulic head as the geophysical parameters of concern. The data for the study were obtained from the Waste Isolation and Pilot Plant (WIPP) site and surrounding area in the remote Chihuahuan Desert of southeastern New Mexico. The central technique was the Radial Basis Function algorithm for an Artificial Neural Network (RBF-ANN). An appraisal of its performance in light of classical and temporal geostatistical techniques is presented. Our classical geostatistical technique of concern was Ordinary Kriging (OK), while the method of Bayesian Maximum Entropy (BME) constituted an advanced, spatio-temporal mapping technique. A fusion technique for soft or inter-dependent data was developed in this study for use with the neural network. It was observed that the RBF-ANN is capable of hydrologic inversion for transmissivity estimation with features remaining essentially similar to that obtained from kriging. The BME technique, on the other hand, was found to reveal an ability to map localized lows and highs that were otherwise not as apparent in OK or RBF-ANN techniques.     

径向基函数(RBF)神经网络及其应用

介绍了径向基函数(RBF)神经网络的原理、 学习算法及其在地震预报专家系统ESEP 3.0中的应用。 实际应用结果表明, 该神经网络可以很好地克服BP神经网络学习过程的收敛过分依赖于初值和可能出现局部收敛的缺陷, 具有较快的运算速度、 较强的非线性映射能力和较好的预报效能。     

Overbank deposition along the concave side of the Red River meanders,Manitoba, and its geomorphic significance

Slow earth sliding is pervasive along the concave side of Red River meanders that impinge on Lake Agassiz glaciolacustrine deposits. These failures form elongated, low‐angled (c. 6 to 10°) landslide zones along the valleysides. Silty overbank deposits that accumulated during the 1999 spring freshet extend continuously along the landslide zones over hundreds of metres and aggraded the lower slopes over a distance 50 to 80 m from the channel margin. The aggradation is not obviously related to meander curvature or location within a meander. Along seven slope profiles surveyed in 1999 near Letellier, Manitoba, the deposits locally are up to 21 cm thick and generally thin with increasing distance from, and height above, the river. Local deposit thickness relates to distance from the channel, duration of inundation of the landslide surface, mesotopography, and variations in vegetation cover. Immediately adjacent to the river, accumulated overbank deposits are up to 4 m thick. The 1999 overbank deposits also were present along the moderately sloped (c. 23 to 27°) concave banks eroding into the floodplain, but the deposits are thinner (locally up to c. 7 cm thick) and cover a narrower area (10 to 30 m wide) than the deposits within the landslide zones. Concave overbank deposition is part of a sediment reworking process that consists of overbank aggradation on the landslide zones, subsequent gradual downslope displacement from earth sliding, and eventually reworking by the river at the toe of the landslide. The presence of the deposits dampens the outward migration of the meanders and contributes to a low rate of contemporary lateral channel migration. Concave overbank sedimentation occurs along most Red River meanders between at least Emerson and St. Adolphe, Manitoba. © Her Majesty the Queen in right of Canada.     

Empirical prediction of coseismic landslide dam formation

In this study we develop an empirical method to estimate the volume threshold for predicting coseismic landslide dam formation using landscape parameters obtained from digital elevation models (DEMs). We hypothesize that the potential runout and volume of landslides, together with river features, determine the likelihood of the formation of a landslide dam. To develop this method, a database was created by randomly selecting 140 damming and 200 non‐damming landslides from 501 landslide dams and > 60 000 landslides induced by the M_w 7.9 2008 Wenchuan earthquake in China. We used this database to parameterize empirical runout models by stepwise multivariate regression. We find that factors controlling landslide runout are landslide initiation volume, landslide type, internal relief (H) and the H/L ratio (between H and landslide horizontal distance to river, L). In order to obtain a first volume threshold for a landslide to reach a river, the runout regression equations were converted into inverse volume equations by taking the runout to be the distance to river. A second volume threshold above which a landslide is predicted to block a river was determined by the correlation between river width and landslide volume of the known damming landslides. The larger of these two thresholds was taken as the final damming threshold. This method was applied to several landslide types over a fine geographic grid of assumed initiation points in a selected catchment. The overall prediction accuracy was 97.4% and 86.0% for non‐damming and damming landslides, respectively. The model was further tested by predicting the damming landslides over the whole region, with promising results. We conclude that our method is robust and reliable for the Wenchuan event. In combination with pre‐event landslide susceptibility and frequency–size assessments, it can be used to predict likely damming locations of future coseismic landslides, thereby helping to plan emergency response. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

A precipiton‐based approach to model hydro‐sedimentary hazards induced by large sediment supplies in alluvial fans

Mountain ranges are frequently subjected to mass wasting events triggered by storms or earthquakes and supply large volumes of sediment into river networks. Besides altering river dynamics, large sediment deliveries to alluvial fans are known to cause hydro‐sedimentary hazards such as flooding and river avulsion. Here we explore how the sediment supply history affects hydro‐sedimentary river and fan hazards, and how well can it be predicted given the uncertainties on boundary conditions. We use the 2D morphodynamic model Eros with a new 2D hydrodynamic model driven by a sequence of flood, a sediment entrainment/transport/deposition model and a bank erosion law. We first evaluate the model against a natural case: the 1999 Mount Adams rock avalanche and subsequent avulsion on the Poerua river fan (West Coast, New Zealand). By adjusting for the unknown sediment supply history, Eros predicts the evolution of the alluvial riverbed during the first post‐landslide stages within 30 cm. The model is subsequently used to infer how the sediment supply volume and rate control the fan aggradation patterns and associated hazards. Our results show that the total injected volume controls the overall levels of aggradation, but supply rates have a major control on the location of preferential deposition, avulsion and increased flooding risk. Fan re‐incision following exhaustion of the landslide‐derived sediment supply leads to sediment transfer and deposition downstream and poses similar, but delayed, hydro‐sedimentary hazards. Our results demonstrate that 2D morphodynamics models are able to capture the full range of hazards occurring in alluvial fans including river avulsion aggradation and floods. However, only ensemble simulations accounting for uncertainties in boundary conditions (e.g., discharge history, initial topography, grain size) as well as model realization (e.g., non‐linearities in hydro‐sedimentary processes) can be used to produce probabilistic hazards maps relevant for decision making. Copyright © 2017 John Wiley & Sons, Ltd.     

起伏地表QR径向基函数有限差分及其在弹性波逆时偏移中的应用

弹性波逆时偏移不受倾角和偏移孔径的限制, 能够实现任意复杂构造的高精度多波成像, 是目前最精确的多分量资料偏移成像方法之一.逆时偏移算法的核心是波场延拓, 传统波场延拓以水平基准面为边界条件, 基于固定采样步长进行规则网格剖分, 采用阶梯近似法处理起伏地表和复杂构造界面时会产生台阶散射, 严重影响起伏地表复杂构造的成像精度.基于无网格节点模型, 定量分析了弹性波模拟中径向基函数有限差分法的频散关系和稳定性条件.基于此, 提出一种基于QR径向基函数的高精度有限差分方法, 并提出一种优化的起伏地表自适应节点剖分方法, 推导了精确的无网格自由边界条件和弹性波无网格混合吸收边界条件, 形成了新的基于无网格的起伏地表弹性波数值模拟方法.此外, 本文将此无网格径向基函数有限差分方法应用于精确的纵横波场矢量分解公式, 实现了起伏地表弹性波逆时偏移成像.通过对高斯山丘模型, 起伏凹陷模型和起伏地表Marmousi-2模型进行数值试算, 验证了本文方法的有效性和可行性.

     

On species distribution modelling,spatial scales and environmental flow assessment with Multi–Layer Perceptron Ensembles: A case study on the redfin barbel (Barbus haasi; Mertens, 1925)

Inconsistent performance of Species Distribution Models (SDMs), which may depend on several factors such as the initial conditions or the applied modelling technique, is one of the greatest challenges in ecological modelling. To overcome this problem, ensemble modelling combines the forecasts of several individual models. A commonly applied ensemble modelling technique is the Multi–Layer Perceptron (MLP) Ensemble, which was envisaged in the 1990s. However, despite its potential for ecological modelling, it has received little attention in the development of SDMs for freshwater fish. Although this approach originally included all the developed MLPs, Genetic Algorithms (GA) now allow selection of the optimal subset of MLPs and thus substantial improvement of model performance. In this study, MLP Ensembles were used to develop SDMs for the redfin barbel (Barbus haasi; Mertens, 1925) at two different spatial scales: the micro–scale and the meso–scale. Finally, the potential of the MLP Ensembles for environmental flow (e–flow) assessment was tested by linking model results to hydraulic simulation. MLP Ensembles with a candidate selection based on GA outperformed the optimal single MLP or the ensemble of the whole set of MLPs. The micro–scale model complemented previous studies, showing high suitability of relatively deep areas with coarse substrate and corroborating the need for cover and the rheophilic nature of the redfin barbel. The meso–scale model highlighted the advantages of using cross–scale variables, since elevation (a macro–scale variable) was selected in the optimal model. Although the meso–scale model also demonstrated that redfin barbel selects deep areas, it partially contradicted the micro–scale model because velocity had a clearer positive effect on habitat suitability and redfin barbel showed a preference for fine substrate in the meso–scale model. Although the meso–scale model suggested an overall higher habitat suitability of the test site, this did not result in a notable higher minimum environmental flow. Our results demonstrate that MLP Ensembles are a promising tool in the development of SDMs for freshwater fish species and proficient in e–flow assessment.     

Including root reinforcement variability in a probabilistic 3D stability model

Forests play a significant role in protecting people, settlements in mountainous terrains from hydrogeomorphic hazards, including shallow landslides. Although several studies have investigated the interactions between forests and slope instabilities, a full understanding of them has not yet been obtained. Additionally, models that incorporate forest stand properties into slope failure probability analyses have not been developed. In principle, physical‐based models, which are powerful tools for landslide hazard analyses, represent an appropriate approach to linking stand properties and slope stability. However, the reliability of these models depends on numerous parameters that describe highly complex geotechnical and hydrological processes (e.g. potential failure depth, saturation ratio, root reinforcement, etc.) that are difficult to measure and model. In particular, the spatial heterogeneity of root reinforcement remains a problem, and the use of physically based models from a forest management perspective has been limited. This paper presents a procedure for assessing slope stability in terms of the Factor of Safety that accounts for forest stand characteristics such as tree density, average diameter at breast height and minimum distance between trees. The procedure combines a three‐dimensional (3D) slope stability model with an evaluation of the variability of root reinforcement in terms of a probability distribution, according to forest characteristics. Monte Carlo simulation is used to account for the residual uncertainties in both stand characteristics and 3D stability model parameters. The proposed method was applied in a subalpine catchment in the Italian Alps, mainly covered by coniferous forest and characterized by steep slopes and high landslide risk. The results suggest that the procedure is highly reliable, according to landslide inventory maps [area under the ROC curve (AUC) is 0.82 and modified success rate (MSR) is 0.70]. Thus, it represents a promising tool for studying the role of root reinforcement in landslide hazard mapping and guiding forest management from a slope stability perspective. Copyright © 2017 John Wiley & Sons, Ltd.     

A comparision of interpolation methods for producing digital elevation models at the field scale

Digital elevation models have been used in many applications since they came into use in the late 1950s. It is an essential tool for applications that are concerned with the Earth's surface such as hydrology, geology, cartography, geomorphology, engineering applications, landscape architecture and so on. However, there are some differences in assessing the accuracy of digital elevation models for specific applications. Different applications require different levels of accuracy from digital elevation models. In this study, the magnitudes and spatial patterning of elevation errors were therefore examined, using different interpolation methods. Measurements were performed with theodolite and levelling. Previous research has demonstrated the effects of interpolation methods and the nature of errors in digital elevation models obtained with indirect survey methods for small‐scale areas. The purpose of this study was therefore to investigate the size and spatial patterning of errors in digital elevation models obtained with direct survey methods for large‐scale areas, comparing Inverse Distance Weighting, Radial Basis Functions and Kriging interpolation methods to generate digital elevation models. The study is important because it shows how the accuracy of the digital elevation model is related to data density and the interpolation algorithm used. Cross validation, split‐sample and jack‐knifing validation methods were used to evaluate the errors. Global and local spatial auto‐correlation indices were then used to examine the error clustering. Finally, slope and curvature parameters of the area were modelled depending on the error residuals using ordinary least regression analyses. In this case, the best results were obtained using the thin plate spline algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

用径向基函数神经网络方法预报太阳黑子数平滑月均值

简单介绍了径向基函数神经网络方法的原理和应用,发展了用径向基函数（RBF）对平滑月平均黑子数进行预报的方法. 用不同的数据序列对网络进行训练,对未来8个月的平滑月平均黑子数进行预报. 用该方法对第23周开始后的平滑月平均黑子数进行逐月预报,并与实测值进行比较,结果表明随着预报实效的延长预报误差被逐渐放大,该方法可以较准确地做出未来４个月的预报,绝对误差可以控制在20以内,标准差为4.8,相对误差控制在38％以内,大部分相对误差不超过15%（占总预报数的89％）,具有较好的应用价值. 用于网络训练的样本数量对预报结果会产生一定的影响.     

Earthquake-induced landslide displacement attenuation models and application in probabilistic seismic landslide displacement analysis

A landslide displacement (DLL) attenuation model has been developed using spectral intensity and a ratio of critical acceleration coefficient to ground acceleration coefficient. In the development of the model,a New Zealand earthquake record data set with magnitudes ranging from 5.0 to 7.2 within a source distance of 175 km is used. The model can be used to carry out deterministic landslide displacement analysis,and readily extended to carry out probabilistic seismic landslide displacement analysis. DLL attenuation models have also been developed by using earthquake source terms,such as magnitude and source distance,that account for the effects of earthquake faulttype,source type,and site conditions. Sensitivity analyses show that the predicted DLL values from the new models are close to those from the Romeo model that was developed from an Italian earthquake record data set. The proposed models are also applied to an analysis of landslide displacements in the Wenchuan earthquake,and a comparison between the predicted and the observed results shows that the proposed models are reliable,and can be confidently used in mapping landslide potential.     

A preliminary study of the failure mechanisms of cascading landslide dams

Landslide dams commonly form when mass earth or rock movements reach a river channel and cause a complete or partial blockage of the channel.Intense rainfalls can induce upstream flows along a sloping channel that significantly affect downstream landslide dams.If a series of landslide dams are collapsed by incoming mountain torrents(induced by intense rainfall),large debris flows can form in a very short period.Furthermore,the failure of these dams can amplify the magnitude and scale of debris flows in the flow direction.The catastrophic debris flows that occurred in Zhouqu County,China on 8 August 2010 were caused by intense rainfall and the upstream cascading failure of landslide dams along the gullies.Incorporating the role of outburst floods associated with the complete or partial failure of landslide dams is an interesting problem usually beyond the scope of analysis because of the inherent modeling complexity.To understand the cascading failure modes of a series of landslide dams,and the dynamic effect these failures have on the enlargement of debris flow scales,experimental tests are conducted in sloping channels mimicking field conditions,with the modeled landslide dams distributed along a sloping channel and crushed by different upstream flows.The failure modes of three different cascades of landslide dams fully or partially blocking a channel river are parametrically studied.This study illustrates that upstream flows can induce a cascading failure of the landslide dams along a channel.Overtopping is the primary failure mechanism,while piping and erosion can also induce failures for different constructed landslide dams.A cascading failure of landslide dams causes a gradually increasing flow velocity and discharge of the front flow,resulting in an increase in both diameter and percentage of the entrained coarse particles.Furthermore,large landslide blockages can act to enhance the efficiency of river incision,or conversely to induce aggradation of fluvial sediments,depending on the blockage factor of the landslide dams and upstream discharge.     

Metrics for quantifying anthropogenic impacts on geomorphology: road networks

This work tests the capability of a recently published topographic index, the Slope Local Length of Auto‐correlation (SLLAC), to portrait and delineate anthropogenic geomorphologies. The patterns of the anthropogenic pressure are defined considering the road network density and the Urban Complexity Index (UCI). First, the research investigates the changes in the SLLAC in two derived parameters (average SLLAC and the SLLAC surface peak curvature – Spc – per km²) connected to the increasing of the anthropogenic structures. Next, natural and anthropogenic landscapes are clustered and classified. The results show that there is a direct correlation between the road network density and the UCI, and the mean SLLAC per km². However, the Spc is inversely correlated with the anthropogenic pressure (network density and urban complexity). This shows that the surface morphology (slope) of regions presenting anthropogenic structures tends to be well organized (low Spc) and, in general, self‐similar at a long distance (higher average SLLAC). The results of the clustering approach show that the procedure can correctly depict anthropogenic landscapes having a road network density greater than about 3 km/km², also in areas covered by vegetation. This latter result is promising for the use of such a procedure in regions that cannot be seen directly from orthophotos or satellite images. The proposed method can actively capture the alteration produced by road networks on surface morphology identifying different signatures of urban development: exploration and densification networks that are responsible for increasing the local density of the network and expanding the network into new areas, respectively. The effects of this alteration on surface processes could be significant for future research, creating new questions about differences due to human or landscape forcing on Earth surface processes. Copyright © 2015 John Wiley & Sons, Ltd.     

黄河某水电站顺层岩质滑坡形成机理与演化过程

黄河上某水电站坝后存在一大型顺层岩质滑坡.钻探、平硐、槽探和现场勘查勘察资料表明该滑坡具有平面分区和剖面分层的显著特征.运用地质环境系统全过程动态演化的观点,采用力学理论、物理模型、数值模拟等方法,分析了滑坡的形成机理和演化机制:该滑坡是在河流冲刷和地震活动等内外综合作用下因层状岩质斜坡岩体层间错动和溃屈基而形成,其演化具有明显时间和空间差异性的多次滑动.在此基础上采用多种极限平衡计算方法综合评价滑坡各区、各级在天然状态下的稳定状态,并结合滑坡所处的地质环境及其演化特征预测滑坡在雾雨、地震以及二者耦合作用下的演化趋势.