检索结果-南通市图书馆

Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility

Journal of Mountain Science 2022年第6期19卷 1670-1698页

作者： Pedro LIMA stefan steger Thomas GLADE Franny G.MURILLO-GARCIA University of Vienna Institute of Geography and Regional ResearchUniversitatsstrafie 7lowViennaAustria Eurac Research Institute for Earth ObservationDrususallee l/Viale Druso 139100Bozen-BolzanoItaly Institute of Geography National Autonomous University of Mexico(UNAM)Circuito ExteriorCiudad Universitaria04510CoyoacanMexico CityMexico

In recent decades, data-driven landslide susceptibility models(Dd LSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occurrence. The available literature is composed of a wealth of published studies and that has identified a large variety of challenges and innovations in this field. This review presents a comprehensive up-to-date overview focusing on the topic of Dd LSM. This research begins with an introduction of the theoretical aspects of Dd LSM research and is followed by an in-depth bibliometric analysis of 2585 publications. This analysis is based on the Web of Science, Clarivate Analytics database and provides insights into the transient characteristics and research trends within published spatial landslide assessments. Following the bibliometric analysis, a more detailed review of the most recent publications from 1985 to 2020 is given. A variety of different criteria are explored in detail, including research design, study area extent,inventory characteristics, classification algorithms, predictors utilized, and validation technique performed. This section, dealing with a quantitativeoriented review expands the time-frame of the review publication done by Reichenbach et al. in 2018 by also accounting for the four years, 2017-2020. The originality of this research is acknowledged by combining together:(a) a recap of important theoretical aspects of Dd LSM;(b) a bibliometric analysis on the topic;(c) a quantitative-oriented review of relevant publications;and(d) a systematic summary of the findings, indicating important aspects and potential developments related to the Dd LSM research topic. The results show that Dd LSM are used within a wide range of applications with study area extents ranging from a few kilometers to national and even continental scales. In more than 70% of publications, a combination of the predictors, slope angle, aspect and geology are used.

关键词： Review Landslide susceptibility Statistical models Machine learning Bibliometrics

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp

引用

Journal of Mountain Science 2019年第3期16卷 561-580页

作者： Franny G.MURILLO-GARCíA stefan steger Irasema ALCáNTARA-AYALA Institute of Geography National Autonomous University of Mexico (UNAM) Institute for Earth Observation Eurac Research

This study aimed to produce a high-quality landslide susceptibility map for Teziutlán municipality, a landslide-prone region in Mexico, which is characterised by a depositional pyroclastic ramp. The heterogeneous quality of available topographic information(i.e. higher resolution digital elevation model only for a sub-region) encouraged to confront modelling results based on two different study area delineations and two raster resolutions. Input data was based on the larger modelling region L15(163 km2) and smaller S(70 km2; located inside L15) with an associated raster cell size of 15 m(region L15 and S15) and 5 m(region S5). The resulting three data sets(L15, S15 and S5) were included into three differently flexible modelling techniques(Generalized Linear Model-GLM, General Additive Model-GAM, Support Vector Machine-SVM) to produce nine landslide susceptibility models. Preceding variable selection was performed heuristically and supported by an exploratory data analysis. The final models were based on the explanatory variables slope angle, slope aspect, lithology, relative slope position, elevation, convergence index, distance to streams, distance to springs and topographic wetness index. The ability of the models to classify independent test data was elaborated using a k-fold cross validation procedure and the AUROC(Area Under the Receiver Operating Characteristic) metric. In general, all produced landslide susceptibility maps depicted the hillslopes of the ravines, which cut the pyroclastic ramp, as prone to landsliding. The modelling results showed that predictive performances(i.e. AUROC values) slightly increased with an increasing flexibility of the applied modelling technique. Thus, SVM performed best, while the GAM outperformed the GLM. This tendency was most distinctive when modelling with the largest landslide sample size(i.e. data set L15; n = 662 landslides). Non-linear classifiers(GAMs, SVMs) performed slightly better when trained on the basis of lower

关键词： Landslide susceptibility Pyroclastic ramp Logistic regression Generalized Additive Model Support Vector Machine Cross validation

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

引用

Geoscience Frontiers 2021年第6期12卷 262-276页

作者： Qigen Lin Pedro Lima stefan steger Thomas Glade Tong Jiang Jiahui Zhang Tianxue Liu Ying Wang Institute for Disaster Risk Management School of Geographical SciencesNanjing University of Information Science&TechnologyNanjing 210044China Department of Geography and Regional Research University of ViennaUniversitätsstraße 71010 ViennaAustria Institute for Earth Observation Eurac ResearchViale Druso 139100 Bolzano-BozenItaly Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education/Academy of Disaster Reduction and Emergency Management Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China

China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inve

关键词： Statistical modelling Landslide susceptibility Generalized additive model Mixed-effects model China

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Adopting the margin of stability for space–time landslide prediction–A data-driven approach for generating spatial dynamic thresholds

引用

Geoscience Frontiers 2024年第5期15卷 75-92页

作者： stefan steger Mateo Moreno Alice Crespi stefano Luigi Gariano Maria Teresa Brunetti Massimo Melillo Silvia Peruccacci Francesco Marra Lotte de Vugt Thomas Zieher Martin Rutzinger Volkmar Mair Massimiliano Pittore GeoSphere Austria ViennaAustria Center for Climate Change and Transformation Eurac ResearchBolzanoItaly Faculty of Geo-information Science and Earth Observation(ITC) University of TwenteEnschedethe Netherlands Research Institute for Geo-Hydrological Protection National Research Council(CNR-IRPI)PerugiaItaly Department of Geosciences University of PaduaPaduaItaly Institute of Atmospheric Sciences and Climate National Research Council(CNR-ISAC)BolognaItaly Department of Geography University of InnsbruckInnsbruckAustria Department of Natural Hazards Austrian Research Centre for Forests(BFW)InnsbruckAustria Office for Geology and Building Materials Testing Autonomous Province of Bolzano-South TyrolCardanoItaly

Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition *** data-driven methods for assessing landslide susceptibility or for establishing rainfall-triggering thresholds are prevalent,integrating spatiotemporal information for dynamic large-area landslide prediction remains a *** main aim of this research is to generate a dynamic spatial landslide initiation model that operates at a daily scale and explicitly counteracts potential errors in the available landslide *** previous studies focusing on space–time landslide modelling,it places a strong emphasis on reducing the propagation of landslide data errors into the modelling results,while ensuring interpretable *** introduces also other noteworthy innovations,such as visualizing the final predictions as dynamic spatial thresholds linked to true positive rates and false alarm rates and by using animations for highlighting its application potential for hindcasting and *** initial step involves the creation of a spatio-temporally representative sample of landslide presence and absence observations for the study area of South Tyrol,Italy(7400 km2)within well-investigated *** setup entails integrating landslide controls that operate on various temporal scales through a binomial Generalized Additive Mixed *** relationships are then interpreted based on variable importance and partial effect plots,while predictive performance is evaluated through various crossvalidation *** and user-defined probability cutpoints are used to establish quantitative thresholds that reflect both,the true positive rate(correctly predicted landslides)and the false positive rate(precipitation periods misclassified as landslide-inducing conditions).The resulting dynamic maps directly visualize landslide threshold *** model demonstrates high predictive performanc

关键词： Early warning Space-time model Rainfall thresholds Landslide susceptibility,Generalized Additive Mixed Model Forecasting

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

从中欧博士生培养模式比较探索创新型博士的培养

引用

研究生教育研究 2017年第1期 83-87页

作者：成花林黄雨 Elmar Schmaltz stefan steger 同济大学土木工程学院上海200092 维也纳大学地理系

创新能力是博士生培养的核心内容和重要任务,与西方发达国家相比,我国博士生创新能力培养目前还存在一些缺憾。本文通过实地考察和文献调研,深入剖析欧洲博士生培养模式,并从选拔方式、课程设置、导师指导和考核评价四个方面对中欧博士... 详细信息

创新能力是博士生培养的核心内容和重要任务,与西方发达国家相比,我国博士生创新能力培养目前还存在一些缺憾。本文通过实地考察和文献调研,深入剖析欧洲博士生培养模式,并从选拔方式、课程设置、导师指导和考核评价四个方面对中欧博士生的培养模式进行分析比较,提出构建我国创新型博士培养模式的对策建议。

关键词：中欧博士生培养模式创新

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：