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1.
In August 2009,Typhoon Morakot brought a large amount of rainfall with both high intensity and long duration to a vast area of Taiwan.Unfortunately,this resulted in a catastrophic landslide in Hsiaolin Village,Taiwan.Meanwhile,large amounts of landslides were formed in the Jiaopu Stream watershed near the southeast part of the Hsiaolin Village.The Hsiaolin Village access road（Provincial Highway No.21 and Bridge No.8） was completely destroyed by the landslide and consequent debris flow.The major scope of this study is to apply a debris flow model to simulate the disaster caused by the debris flow that occurred in the Jiaopu Stream during Typhoon Morakot.According to the interviews with local residents,this study applied the destruction time of Bridge No.8 and Chen＇s house to verify the numerical debris flow model.By the spatial rainfall distributions information,the numerical simulations of the debris flow are conducted in two stages.In the first stage（before the landslide-dam failure）,the elevation of the debris flow and the corresponding potential damages toward residential properties were investigated.In the second stage（after the landslidedam failure）,comparisons of simulation results between the longitudinal and cross profiles of the Jiaopu Stream were performed using topographic maps and satellite imagery.In summary,applications of the adopted numerical debris flow model have shown positive impact on supporting better understanding of the occurrence and movement of debris flow processes.  相似文献   

2.
Natural dams are formed when landslides are triggered by heavy rainfall during extreme weather events in the mountainous areas of Taiwan.During landslide debris movement, two processes occur simultaneously: the movement of landslide debris from a slope onto the riverbed and the erosion of the debris under the action of high-velocity river flow. When the rate of landslide deposition in a river channel is higher than the rate of landslide debris erosion by the river flow, the landslide forms a natural dam by blocking the river channel. In this study, the effects of the rates of river flow erosion and landslide deposition(termed the erosive capacity and depositional capacity, respectively) on the formation of natural dams are quantified using a physics-based approach and are tested using a scaled physical model.We define a dimensionless velocity index vde as the ratio between the depositional capacity of landslide debris(vd) and the erosive capacity of water flow(ve).The experimental test results show that a landslidedam forms when landslide debris moves at high velocity into a river channel where the river-flow velocity is low, that is, the dimensionless velocity index vde 54. Landslide debris will not have sufficient depositional capacity to block stream flow when the dimensionless velocity index vde 47. The depositional capacity of a landslide can be determined from the slope angle and the friction of the sliding surface, while the erosive capacity of a dam can be determined using river flow velocity and rainfall conditions. The methodology described in this paper was applied to seven landslide dams that formed in Taiwan on 8 August 2009 during Typhoon Morakot,the Tangjiashan landslide dam case, and the YingxiuWolong highway K24 landslide case. The dimensionless velocity index presented in this paper can be used before a rainstorm event occurs to determine if the formation of a landslide dam is possible.  相似文献   

3.
Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.  相似文献   

4.
Rudraprayag in Garhwal Himalayan division is one of the most vulnerable districts to landslides in India. Heavy rainfall, steep slope and developmental activities are important factors for the occurrence of landslides in the district. Therefore, specific assessment of landslide susceptibility and its accuracy at regional level is essential for disaster management and proper land use planning. The article evaluates effectiveness of frequency ratio, fuzzy logic and logistic regression models for assessing landslide susceptibility in Rudraprayag district of Uttarakhand state, India. A landslide inventory map was prepared and verified by field data. Fourteen landslide parameters and generated inventory map were utilized to prepare landslide susceptibility maps through frequency ratio, fuzzy logic and logistic regression models. Landslide susceptibility maps generated through these models were classified into very high, high, medium, low and very low categories using natural breaks classification. Receiver operating characteristics (ROC) curve, spatially agreed area approach and seed cell area index (SCAI) method were used to validate the landslide models. Validation results revealed that fuzzy logic model was found to be more effective in assessing landslide susceptibility in the study area. The landslide susceptibility map generated through fuzzy logic model can be best utilized for landslide disaster management and effective land use planning.  相似文献   

5.
Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups, (i) training dataset and (ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages, distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.  相似文献   

6.
Defining a basin under a critical state(or a self-organized criticality) that has the potential to initiate landslides,debris flows,and subsequent sediment disasters,is a key issue for disaster prevention.The Lushan Hot Spring area in Nantou County,Taiwan,suffered serious sediment disasters after typhoons Sinlaku and Jangmi in 2008,and following Typhoon Morakot in 2009.The basin’s internal slope instability after the typhoons brought rain was examined using the landslide frequency-area distribution.The critical state indices attributed to landslide frequency-area distribution are discussed and the marginally unstable characteristics of the study area indicated.The landslides were interpreted from Spot 5 images before and after disastrous events.The results of the analysis show that the power-law landslide frequency-area curves in the basin for different rainfall-induced events tend to coincide with a single line.The temporal trend of the rainfallinduced landslide frequency-area distribution shows 1/f noise and scale invariance.A trend exists for landslide frequency-area distribution in log-log space for larger landslides controlled by the historical maximum accumulated rainfall brought by typhoons.The unstable state of the basin,including landslides,breached dams,and debris flows,are parts of the basin’s self-organizing processes.The critical state of landslide frequency-area distribution could be estimated by a critical exponent of 1.0.The distribution could be used for future estimation of the potential landslide magnitude for disaster mitigation and to identify the current state of a basin for management.  相似文献   

7.
A detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system(GIS).Known as one of the most landslide-prone areas in China, the Youfang catchment of Longnan mountain region,which lies in the transitional area among QinghaiTibet Plateau, loess Plateau and Sichuan Basin, was selected as a representative case to evaluate the frequency and distribution of landslides.Statistical relationships for landslide susceptibility assessment were developed using landslide and landslide causative factor databases.Logistic regression(LR)was used to create the landslide susceptibility maps based on a series of available data sources: landslide inventory; distance to drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index, and land use.The quality of the landslide susceptibility map produced in this paper was validated and the result can be used fordesigning protective and mitigation measures against landslide hazards.The landslide susceptibility map is expected to provide a fundamental tool for landslide hazards assessment and risk management in the Youfang catchment.  相似文献   

8.
China-Pakistan Economic Corridor(CPEC)is a framework of regional connectivity,which will not only benefit China and Pakistan but will have positive impact on Iran,Afghanistan,India,Central Asian Republic,and the region.The surrounding area in CPEC is prone to frequent disruption by geological hazards mainly landslides in northern Pakistan.Comprehensive landslide inventory and susceptibility assessment are rarely available to utilize for landslide mitigation strategies.This study aims to utilize the high-resolution satellite images to develop a comprehensive landslide inventory and subsequently develop landslide susceptibility maps using multiple techniques.The very high-resolution(VHR)satellite images are utilized to develop a landslide inventory using the visual image classification techniques,historic records and field observations.A total of 1632 landslides are mapped in the area.Four statistical models i.e.,frequency ratio,artificial neural network,weights of evidence and logistic regression were used for landslide susceptibility modeling by comparing the landslide inventory with the topographic parameters,geological features,drainage and road network.The developed landslides susceptibility maps were verified using the area under curve(AUC)method.The prediction power of the model was assessed by the prediction rate curve.The success rate curves show 93%,92.8%,92.7%and 87.4%accuracy of susceptibility maps for frequency ratio,artificial neural network,weights of evidence and logistic regression,respectively.The developed landslide inventory and susceptibility maps can be used for land use planning and landslide mitigation strategies.  相似文献   

9.
已有滑坡敏感性研究中对评价指标的选取可以归结为气象、水文、地形、地质、植被、人类活动等方面,这些因子指标来源不一,在缺少数据资料地区难以完整收集。针对这个问题,考虑到目前DEM数据的广泛可获得性及其对滑坡评价的重要性,本文仅利用DEM数据及其派生因子,研究土质滑坡敏感性评价的可行性。研究中把评价因子分为2组：第1组数据仅由DEM派生,包括高程、坡度、坡向、地形起伏度、曲率、水流强度指数（Stream Power Index, SPI）、沉积运输指数（Sediment Transport Index, STI）、地形湿度指数（Topographic Wetness Index, TWI）;第2组数据作为对照组,除了包括上述DEM派生的8个因子外,同时加入植被覆盖度、土地利用、土壤类型、年均降雨量因子。本文分别选取逻辑回归模型和证据权法,基于上述2组评价因子,以德化县为例对比2组因子评价结果,利用第1组和第2组数据进行滑坡敏感性评价,结果精度分别为73%和83%。结果表明,仅利用DEM数据进行土质滑坡敏感性评价方法可行,可以为缺乏资料区滑坡敏感性评价提供借鉴。  相似文献   

10.
滑坡灾害成因机理复杂、影响因素众多,深度学习作为当前人工智能领域的热点,能够更好地模拟滑坡灾害的形成并准确预测潜在的斜坡。为了挖掘深度学习在滑坡易发性的应用潜能,本文构建了一维、二维和三维的滑坡数据表达形式,并提出3种基于卷积神经网络模型（Convolutional Neural Networks, CNN）的滑坡易发性分析处理框架：基于CNN分类器、基于CNN与逻辑回归的融合和基于CNN集成,最后以江西省铅山县为研究对象进行验证,结果表明：所有基于CNN的易发性模型都能够获得准确且可靠的滑坡易发性分析结果。其中,基于二维数据的CNN模型在所有单分类器中预测精度最高,为78.95%。此外,二维CNN特征提取能够显著提升逻辑回归的预测精度,其准确率提升7.9%。最后,异质集成策略能够大幅度提升基于CNN分类器的滑坡预测精度,其准确率提升4.35%~8.78%。  相似文献   

11.
Wudu County in northwestern China frequently experiences large-scale landslide events.High-magnitude earthquakes and heavy rainfall events are the major triggering factors in the region.The aim of this research is to compare and combine landslide susceptibility assessments of rainfalltriggered and earthquake-triggered landslide events in the study area using Geographical Information System(GIS) and a logistic regression model.Two separate susceptibility maps were produced using inventories reflecting single landslide-triggering events,i.e.,earthquakes and heavy rain storms.Two groups of landslides were utilized: one group containing all landslides triggered by extreme rainfall events between 1995 and 2003 and the other group containing slope failures caused by the 2008 Wenchuan earthquake.Subsequently,the individual maps were combined to illustrate the locations of maximum landslide probability.The use of the resulting three landslide susceptibility maps for landslide forecasting,spatial planning and for developing emergency response actions are discussed.The combined susceptibility map illustrates the total landslide susceptibility in the study area.  相似文献   

12.
Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.  相似文献   

13.
Newmark位移模型是研究地震滑坡易发性的经典模型,机器学习方法支持向量机模型也越来越多的应用到滑坡易发性评估研究。本文将Newmark位移模型与支持向量机模型相结合,建立基于物理机理的地震滑坡易发性评估模型并应用于2008年汶川地震重灾区汶川县。从震后遥感影像目视解译出汶川县1900处地震诱发滑坡,并将其随机划分为70%的训练数据集和30%的验证数据集。选择地形起伏度、坡度、地形曲率、与构造断裂带距离、与水系距离、与道路距离6个因子与Newmark位移值共同作为地震滑坡易发性影响因素。利用ROC曲线和模型不确定性等指标对模型结果进行评估,并与二元统计模型频率比和多元统计模型Logistic回归的结果进行对比。结果表明：与频率比和Logistic回归模型相比,支持向量机模型的正确率最高,训练集和验证集ROC曲线下的面积分别为0.876和0.851。将模型应用于绘制汶川县地震滑坡易发性图,结果显示滑坡易发性图与实际的滑坡点位分布一致性较高,有80.4%的滑坡位于极高和高易发区。这说明支持向量机与Newmark位移方法结合建立的地震滑坡易发性评估模型有较高的预测价值,可以为滑坡风险评估和管理提供依据。  相似文献   

14.
Daniau Village in Daniau Creek Watershed, Taitung County, Taiwan, sustained damages from landslides and mudflows during Typhoon Morakot in 2009. The purpose of this study is to adopt the FLO-2D numerical model recognized by Federal Emergency Management Agency (FEMA) to simulate the mudflow, and the Daniau Village was used as a case study, along with rainfall and digital terrain data for this simulation. On the basis of sediment yields, the residual sediment volume in the landslide area was determined to be 33,276 m3 by comparison of digital elevation models (DEMs) and by using the universal soil loss equation (USLE). In addition, this study performed a hydrological frequency analysis of rainfall to estimate the flow discharge as conditions of the simulation. Results of disaster surveys were collected to compare with outputs of the numerical model. Results of the simulation conducted with FLO-2D indicated that if the countermeasure was not destroyed, the drainage work would function without overflow. This study aimed to review the effectiveness of countermeasure on the basis of simulation results obtained by using the model to provide references for future disaster prevention and resident evacuation plans.  相似文献   

15.
In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.  相似文献   

16.
不同的易发性评价模型可以得到有差异的滑坡空间预测结果，选取最优模型甚至综合各模型的优势是提高易发性评价精度的有效方法。为检验模型融合思路的有效性，以鄂西地区五峰县渔洋关镇为研究区，提取坡度、地层、断层、河流、公路等7个滑坡成因条件，分别采用信息量模型、证据权模型和频率比模型进行滑坡易发性评价；并将3种模型分别进行归一化、主成分分析（PCA，Principal component analysis）和优势融合，得到了6幅易发性分区图。结果表明:优势耦合模型精度最高（90.3%），频率比模型次之（89.7%），归一化融合模型和PCA融合模型分别为89.3%和89.1%，以上4种结果的精度均高于证据权模型（87.7%）和信息量模型（87.6%）；6幅预测图对应的评价结论与历史滑坡空间分布的实际情况相符。空间一致性对比结论表明，主成分融合模型与优势耦合模型的同格率高达68%，其预测结果避免了单个模型预测结论带来的偶然性和片面性，说明多模型融合方法与优势耦合模型在提高滑坡易发性预测精度上是可行性的，该思路对其他地区滑坡灾害易发性评价具有借鉴意义。   相似文献   

17.
区域滑坡易发性评价对滑坡灾害防治具有重要意义，贵州省思南县由于其特殊的自然地理和地质条件，受滑坡地质灾害的影响非常严重，因此，非常有必要对思南县的滑坡易发性进行评价。在滑坡编录的基础上，采用由RS、GIS和GPS组成的3S技术，获取了思南县的数字高程模型、坡度、坡向、剖面曲率、坡长、岩土类型、地表湿度指数、距离水系的距离、植被覆盖度和地表建筑物指数10个滑坡影响因子；再在频率比和相关性分析的基础上，利用逻辑回归模型对思南县的滑坡易发性进行了评价并绘制了易发性分布图。结果表明:利用逻辑回归模型预测思南县滑坡易发性的准确率(AUC值)达到0.797，较为准确地预测出了思南县滑坡分布规律；极高和高滑坡易发区主要分布在高程低于600 m、地表坡度较大且以软质岩类为主的区域；而极低和低滑坡易发区主要分布在高程较高、地表坡度较小且以硬质岩类为主的区域。   相似文献   

18.
不同机器学习预测滑坡易发性的建模过程及其不确定性有所差异, 另外如何有效识别滑坡易发性的主控因子意义重大。针对上述问题, 以支持向量机(support vector machine, 简称SVM)和随机森林(random forest, 简称RF)为例探讨了基于机器学习的滑坡易发性预测及其不确定性, 创新地提出了"权重均值法"来综合计算出更准确的滑坡主控因子。首先获取陕西省延长县滑坡编录和10类基础环境因子, 将因子频率比值作为SVM和RF的输入变量; 再将滑坡与随机选择的非滑坡样本划分为训练集和测试集, 用训练好的机器学习预测出滑坡易发性并制图; 最后用受试者工作曲线、均值和标准差等来评估建模不确定性, 并计算滑坡主控因子。结果表明: ①机器学习能有效预测出区域滑坡易发性, RF预测的滑坡易发性精度高于SVM, 而其不确定性低于SVM, 但两者的易发性分布规律整体相似; ②权重均值法计算出延长县滑坡主控因子依次是坡度、高程和岩性。实例分析和文献综述显示RF模型相较于其他机器学习模型属于可靠性较高的易发性模型。   相似文献   

19.
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.  相似文献   

20.
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。  相似文献   

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