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1.
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。 相似文献
2.
Validation of an artificial neural network model for landslide susceptibility mapping 总被引：6，自引：3，他引：3
The aim of this study was to validate an artificial neural network model at Youngin, Janghung, and Boeun, Korea, using the
geographic information system (GIS). The factors that influence landslide occurrence, such as the slope, aspect, curvature,
and geomorphology of topography, the type, material, drainage, and effective thickness of soil, the type, diameter, age, and
density of forest, distance from lineament, and land cover were either calculated or extracted from the spatial database and
Landsat TM satellite images. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the
artificial neural network model. The landslide susceptibility analysis results were validated and cross-validated using the
landslide locations as study areas. For this purpose, weights for each study area were calculated by the artificial neural
network model. Among the nine cases, the best accuracy (81.36%) was obtained in the case of the Boeun-based Janghung weight,
whereas the Janghung-based Youngin weight showed the worst accuracy (71.72%). 相似文献
3.
For quantitative landslide susceptibility mapping, this study applied and verified a frequency ratio, logistic regression,
and artificial neural network models to Pemalang area, Indonesia, using a Geographic Information System (GIS). Landslide locations
were identified in the study area from interpretation of aerial photographs, satellite imagery, and field surveys; a spatial
database was constructed from topographic and geological maps. The factors that influence landslide occurrence, such as slope
gradient, slope aspect, curvature of topography, and distance from stream, were calculated from the topographic database.
Lithology was extracted and calculated from geologic database. Using these factors, landslide susceptibility indexes were
calculated by frequency ratio, logistic regression, and artificial neural network models. Then the landslide susceptibility
maps were verified and compared with known landslide locations. The logistic regression model (accuracy 87.36%) had higher
prediction accuracy than the frequency ratio (85.60%) and artificial neural network (81.70%) models. The models can be used
to reduce hazards associated with landslides and to land-use planning. 相似文献
4.
投影寻踪是一种降维处理技术,它可以将高维分析问题通过投影方向转化为低维问题分析.应用该法的关键在于寻求最佳投影方向,这可以转化为一个复杂的非线性优化问题,结合Matlab的遗传算法工具箱进行优化求解.本文以淮南新庄孜煤矿为例,建立突水水源判别投影寻踪模型,并与模糊综合评判模型、神经网络模型、灰色聚类模型进行分析比较.结果表明:投影寻踪判别模型能够有效地判别突水水源,比模糊综合评判、神经网络模型、灰色聚类模型具有更高的准确性,为矿井突水水源判别提供了一个新途径. 相似文献
5.
6.
Moung-Jin Lee Jae-Won Choi Hyun-Joo Oh Joong-Sun Won Inhye Park Saro Lee 《Environmental Earth Sciences》2012,67(1):23-37
Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model. 相似文献
7.
边坡位移是滑坡演化的宏观体现,分析并预测滑坡位移发展态势对于防灾减灾具有重要意义。由于滑坡位移曲线具有明显的非线性特征,单一模型往往难以刻画其非线性与复杂性。为发展一种普遍适用于滑坡位移的预测方法,提出了一种联合多种数据驱动模型的新方法。该方法根据时间序列分析理论,将滑坡位移序列分解为趋势项和周期项,趋势项采用并联型灰色神经网络处理,周期项则采用人工蜂群算法（ABC）优化后的极限学习机模型（ELM）处理,从而充分应用各种模型的优点。以三峡库区白水河和八字门滑坡为例,对位移数据进行分析处理后,灰色神经网络模型预测其趋势性位移,改进后的极限学习机模型对周期性位移进行训练及预测。结果表明:在预测精度上,优化后的极限学习机模型准确度高于极限学习机模型及小波神经网络等方法,提出的灰色神经网络与ABC-ELM的组合模型可作为实际工程的一个参考。 相似文献
8.
The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea 总被引：13，自引：0，他引：13
The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility. 相似文献
9.
滑坡周期项位移的预测,是研究地质灾害中滑坡变形至关重要的一步。由于单一模型易受偶然因素影响,且无法充分利用有效信息,导致其预测精度不高,适用性不强。基于此,文中提出了一种结合自适应粒子群算法（APSO）、支持向量机回归算法（SVR）、门控神经网络算法(GRU)的组合模型。该模型通过自适应粒子群优化算法对支持向量机回归算... 相似文献
10.
滑坡位移预测模型是滑坡预警系统建立的核心,而模型可靠性与精确性关键在于主控因子的选取与基础理论模型的构建。学者们通过大量滑坡实例研究,已取得了诸多成果,但是由于滑坡位移变化具有强烈的个性特征及趋势发展的不确定性问题,在多因子联合作用下的位移预测模型尚有不足之处。本文以西南地区普遍存在的平推式滑坡——垮梁子滑坡为研究对象,结合前人已有的研究成果,综合考虑坡体内外各项影响因子,利用灰色关联度与相关性分析对坡体变形主控因子进行优化筛选。以此为基础,提出一种基于GM（1,1）灰色模型与改进型自适应遗传算法（IAGA）进行优化的小波神经网络（WNN）联合预测模型构建方案。通过对垮梁子滑坡历时5年的监测数据挖掘分析,得知滑坡变形受累计降雨、渗压、地下水位及土体含水率影响显著,预测结果与实际监测比较吻合。相较于传统BP神经网络模型、小波神经网络模型和未优化遗传算法-小波神经网络联合模型,该联合模型具有更好的稳定性与精度优势,在滑坡预警预报研究中具有良好的应用前景。 相似文献
11.
Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models 总被引：23，自引：5，他引：18
This paper summarizes findings of landslide hazard analysis on Penang Island, Malaysia, using frequency ratio, logistic regression, and artificial neural network models with the aid of GIS tools and remote sensing data. Landslide locations were identified and an inventory map was constructed by trained geomorphologists using photo-interpretation from archived aerial photographs supported by field surveys. A SPOT 5 satellite pan sharpened image acquired in January 2005 was used for land-cover classification supported by a topographic map. The above digitally processed images were subsequently combined in a GIS with ancillary data, for example topographical (slope, aspect, curvature, drainage), geological (litho types and lineaments), soil types, and normalized difference vegetation index (NDVI) data, and used to construct a spatial database using GIS and image processing. Three landslide hazard maps were constructed on the basis of landslide inventories and thematic layers, using frequency ratio, logistic regression, and artificial neural network models. Further, each thematic layer’s weight was determined by the back-propagation training method and landslide hazard indices were calculated using the trained back-propagation weights. The results of the analysis were verified and compared using the landslide location data and the accuracy observed was 86.41, 89.59, and 83.55% for frequency ratio, logistic regression, and artificial neural network models, respectively. On the basis of the higher percentages of landslide bodies predicted in very highly hazardous and highly hazardous zones, the results obtained by use of the logistic regression model were slightly more accurate than those from the other models used for landslide hazard analysis. The results from the neural network model suggest the effect of topographic slope is the highest and most important factor with weightage value (1.0), which is more than twice that of the other factors, followed by the NDVI (0.52), and then precipitation (0.42). Further, the results revealed that distance from lineament has the lowest weightage, with a value of 0. This shows that in the study area, fault lines and structural features do not contribute much to landslide triggering. 相似文献
12.
Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia 总被引：20，自引：6，他引：14
This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information
system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial
photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed
into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope,
topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall,
land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed
using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined
by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation
weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model
suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the
other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to
validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation
results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas. 相似文献
13.
Use of an artificial neural network for analysis of the susceptibility to landslides at Boun,Korea 总被引：13，自引：0，他引：13
The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the resulting techniques to the study area of Boun in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs and field survey data. A spatial database of the topography, soil type, timber cover, geology, and land cover was constructed and the landslide-related factors were extracted from the spatial database. Using these factors, the susceptibility to landslides was analyzed by artificial neural network methods. The results of the landslide susceptibility maps were compared and verified using known landslide locations at another area, Yongin, in Korea. A Geographic Information System (GIS) was used to analyze efficiently the vast amount of data and an artificial neural network turned out to be an effective tool to analyze the landslide susceptibility. 相似文献
14.
区域滑坡易发性研究对地质灾害风险管理具有重要意义.以往研究中,将多元统计模型与机器学习方法相结合用于滑坡易发性评价的研究较少.以三峡库区万州区为例,首先选取9种指标因子（坡度、坡向、剖面曲率、地表纹理、地层岩性、斜坡结构、地质构造、水系分布及土地利用类型）作为滑坡易发性评价指标.基于证据权模型（weights of evidence,WOE）计算得到的对比度和滑坡面积比与分级面积比的相对大小,对各指标因子进行状态分级;再利用粒子群法优化的BP神经网络模型（PSO-BP）得到各指标因子权重.综合两种模型确定的状态分级权重和指标因子权重（WOE-BP）计算滑坡易发性指数（landslide susceptibility index,LSI）,基于GIS平台得到全区滑坡易发性分区图.结果表明:水系、地层岩性和地质构造是影响万州区滑坡发育的主要指标因子;WOE-BP模型的预测精度为80.8%,优于WOE模型的73.1%和BP神经网络模型的71.6%,可为定量计算指标因子权重和优化滑坡易发性评价提供有效途径. 相似文献
15.
基于RES理论的潜在滑坡识别 总被引：1，自引：0，他引：1
潜在滑坡的识别涉及到多种因素间的相互作用和相互耦合,常规方法难以准确描述影响因素间相互耦合作用对斜坡稳定性的影响。结合前人的研究成果,引入岩石工程系统（RES）理论的交互作用矩阵构造和编码原理,提出了基于BP网络潜在滑坡识别模型,推导了在该识别模型下实现交互作用矩阵的编码及考虑多因素相互作用权重确定的流程,并将其运用于潜在土质滑坡判别中。研究结果表明:运用此识别模型,不仅能够实现对潜在滑坡识别,同时能够实现基于多因素交互作用影响的各识别指标权重的确定。 相似文献
16.
Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya 总被引：11，自引：5，他引：6
Chandra Prakash Poudyal Chandong Chang Hyun-Joo Oh Saro Lee 《Environmental Earth Sciences》2010,61(5):1049-1064
This study considers landslide susceptibility mapping by means of frequency ratio and artificial neural network approaches
using geographic information system (GIS) techniques as a basic analysis tool. The selected study area was that of the Panchthar
district, Nepal. GIS was used for the management and manipulation of spatial data. Landslide locations were identified from
field survey and aerial photographic interpretation was used for location of lineaments. Ten factors in total are related
to the occurrence of landslides. Based on the same set of factors, landslide susceptibility maps were produced from frequency
ratio and neural network models, and were then compared and evaluated. The weights of each factor were determined using the
back-propagation training method. Landslide susceptibility maps were produced from frequency ratio and neural network models,
and they were then compared by means of their checking. The landslide location data were used for checking the results with
the landslide susceptibility maps. The accuracy of the landslide susceptibility maps produced by the frequency ratio and neural
networks is 82.21 and 78.25%, respectively. 相似文献
17.
Determination and application of the weights for landslide susceptibility mapping using an artificial neural network 总被引：38，自引：0，他引：38
The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping. 相似文献
18.
Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley,Italy 总被引：6，自引：3，他引：3
F. Falaschi F. Giacomelli P. R. Federici A. Puccinelli G. D’Amato Avanzi A. Pochini A. Ribolini 《Natural Hazards》2009,50(3):551-569
This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial
neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed
through statistical models (conditional analysis and logistic regression), and neural network application, in order to better
understand the relationship between the geological/geomorphological landforms and processes and landslide occurrence, and
to increase the performance of landslide susceptibility models. The proposed experimental study concerns with a wide research
project, promoted by the Tuscany Region Administration and APAT-Italian Geological Survey, aimed at defining the landslide
hazard in the area of the Sheet 250 “Castelnuovo di Garfagnana” (1:50,000 scale). The study area is located in the middle
part of the Serchio River basin and is characterized by high landslide susceptibility due to its geological, geomorphological,
and climatic features, among the most severe in Italy. Terrain susceptibility to slope failure has been approached by means
of indirect-quantitative statistical methods and neural network software application. Experimental results from different
methods and the potentials and pitfalls of this methodological approach have been presented and discussed. Applying multivariate
statistical analyses made it possible a better understanding of the phenomena and quantification of the relationship between
the instability factors and landslide occurrence. In particular, the application of a multilayer neural network, equipped
for supervised learning and error control, has improved the performance of the model. Finally, a first attempt to evaluate
the classification efficiency of the multivariate models has been performed by means of the receiver operating characteristic
(ROC) curves analysis approach. 相似文献
19.
长江三峡工程库区巴东新城址环境工程地质预测 总被引：2，自引：0，他引：2
巴东县城新址是长江三峡工程库区移民城镇环境工程地质问题较突出的场址之一, 主要环境工程地质问题是古滑坡的失稳和库岸再造。在查清地质环境条件的基础上, 本文着重进行了黄土坡古滑坡、赵树岭古滑坡等古滑坡的稳定性预测, 并根据实测的有关参数, 采用图解法预测库岸再造宽度。在此基础上, 根据影响该区城市开发建设的主要的地质环境要素, 建立隶属函数, 采用模糊综合评判模型及算法, 给出巴东县城新址区的城市开发建设的环境地质分区评价。 相似文献
20.
基于遗传算法和模糊神经网络的边坡稳定性评价 总被引：4，自引：0，他引：4
边坡工程是一个动态的、模糊的、开放的复杂非线性系统,传统的分析方法有时难以对复杂边坡的稳定性做出符合实际的评价。影响边坡稳定性的因素复杂且具有随机性和模糊性。由于神经网络方法不仅能考虑定量因素,而且能考虑定性因素的影响,因而神经网络方法适用于解决非确定性的边坡稳定性评价问题。综合考虑影响边坡稳定性的各方面因素,建立了基于遗传算法的模糊神经网络模型,并利用大量工程资料对网络进行训练和测试。预测结果表明,该模型的预测精度明显高于目前同类方法。 相似文献