京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22125号 / 工博第4655号 / 新制||工||1726(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 渦岡 良介, 教授 角 哲也, 准教授 佐山 敬洋 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・論文博士 / 博士(工学) / 乙第13173号 / 論工博第4164号 / 新制||工||1699(附属図書館) / (主査)教授 寶 馨, 教授 渦岡 良介, 准教授 佐山 敬洋 / 学位規則第4条第2項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Yes / Modern early warning system (EWS) requires sophisticated knowledge of the natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and closely linked to a variety of other hazards. EWS for landslides prediction and detection relies on scientific methods and models which requires input from the time series data, such as the earth observation (EO) and urban environment data. Such data sets are produced by a variety of remote sensing satellites and Internet of things sensors which are deployed in the landslide prone areas. To this end, the automatic discovery of potential time series data sources has become a challenge due to the complexity and high variety of data sources. To solve this hard research problem, in this paper, we propose a novel ontology, namely Landslip Ontology, to provide the knowledge base that establishes relationship between landslide hazard and EO and urban data sources. The purpose of Landslip Ontology is to facilitate time series data source discovery for the verification and prediction of landslide hazards. The ontology is evaluated based on scenarios and competency questions to verify the coverage and consistency. Moreover, the ontology can also be used to realize the implementation of data sources discovery system which is an essential component in EWS that needs to manage (store, search, process) rich information from heterogeneous data sources.

Earthquake triggered landslides cause loss of life, destroy structures, roads, powerlines, and pipelines and therefore they have a direct impact on the social and economic life of the hazard region. The damage and fatalities directly related to strong ground shaking and fault rupture are sometimes exceeded by the damage and fatalities caused by earthquake triggered landslides. Even though future earthquakes can hardly be predicted, the identification of areas that are highly susceptible to landslide hazards is possible. For geographical information systems (GIS) based deterministic slope stability and earthquake-induced landslide analysis, the grid-cell approach has been commonly used in conjunction with the relatively simple infinite slope model. The infinite slope model together with Newmark's displacement analysis has been widely used to create seismic landslide susceptibility maps. The infinite slope model gives reliable results in the case of surficial landslides with depth-length ratios smaller than 0.1. On the other hand, the infinite slope model cannot satisfactorily analyze deep-seated coherent landslides. In reality, coherent landslides are common and these types of landslides are a major cause of property damage and fatalities. In the case of coherent landslides, two- or three-dimensional models are required to accurately analyze both static and dynamic performance of slopes. These models are rarely used in GIS-based landslide hazard zonation because they are numerically expensive compared to one dimensional infinite slope models. Building metamodels based on data obtained from computer experiments and using computationally inexpensive predictions based on these metamodels has been widely used in several engineering applications. With these soft computing methods, design variables are carefully chosen using a design of experiments (DOE) methodology to cover a predetermined range of values and computer experiments are performed at these chosen points. The design variables and the responses from the computer simulations are then combined to construct functional relationships (metamodels) between the inputs and the outputs. In this study, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are used to predict the static and seismic responses of slopes. In order to integrate the soft computing methods with GIS for coherent landslide hazard analysis, an automatic slope profile delineation method from Digital Elevation Models is developed. The integrated framework is evaluated using a case study of the 1989 Loma Prieta, CA earthquake (Mw = 6.9). A seismic landslide hazard analysis is also performed for the same region for a future scenario earthquake (Mw = 7.03) on the San Andreas Fault.

Cette étude a été réalisée dans les Alpes-Maritimes, dans le SE de la France, avec le Bar-Sur-Loup comme zone d'étude, afin de tester une approche multidisciplinaire d’analyse de risque, de vulnérabilité et de résilience dans le domaine des risques naturels. L'objectif principal de cette étude est d’identifier et d’analyser l’évolution des glissements de terrain superficiels, en réponse aux différents scénarios d’évolution de la quantité d’eau souterraine en fonction de la variation du climat régional, et d’étudier la vulnérabilité territoriale de différents éléments de notre zone d’étude exposés à ces glissements de terrains de surface, combiné à une approche de résilience territoriale. Les modèles conceptuels de vulnérabilité territoriale et de la résilience ont été développés. Les résultats ont été présentés sous la forme de cartes de aléas, la vulnérabilité, la résilience et risque territorial / This study was conducted within Alps-Maritimes located SE of France, where Le Bar-Sur-Loup was pilot study area to test multidisciplinary approach to analyze hazard, vulnerability and resilience under risk domains. The principal aim of this study was to use an integrated approach and methodology to identify and analyze shallow landslides evolution in response to different ground water rise scenarios due to regional climate variability, and predicts territorial vulnerabilities of different territorial elements exposed to shallow landslides hazard with combine approach of territorial resilience. Conceptual model of territorial vulnerability and territorial resilience were developed based on identified territorial elements. Results were presented as maps of hazard, territorial vulnerability, and territorial resilience and risk

Landslide hazard assessment of densely forested, remote, and difficult to access areas can be rapidly accomplished with airborne light detection and ranging (lidar) data. An evaluation of geomorphic change by lidar-derived digital elevation models (DEMs) coupled with geotechnical soils analysis, aerial photographs, ground measurements, precipitation data, and numerical modeling can provide valuable insight to the reactivation process of unstable landslides. A landslide was selected based on previous work by Mickleson (2011) and Burns et al. (2010) that identified the Madrone Landslide with significant volumetric changes. This study expands on previous work though an evaluation of the timing and causation of slope failure of the Madrone Landslide. The purpose of this study was to evaluate landslide morphology, precipitation data, historical aerial photographs, ground crack measurements, geotechnical properties of soil, numerical modeling, and elevation data (with multi-temporal lidar data), to determine the conditions associated with failure of the Madrone Landslide. To evaluate the processes involved and timing of slope failure events, a deep seated potentially unstable landslide, situated near the contact of Eocene sedimentary and volcanic rocks, was selected for a detailed analysis. The Madrone Landslide (45.298383/-123.338796) is located in Yamhill County, about 12 kilometers west of Carlton, Oregon. Site elevation ranges from 206 meters (m) North American Vertical Datum (NAVD-88) near the head scarp to 152 m at the toe. The landslide is composed of two parts, an upper more recent rotational slump landslide and a lower much older earth flow landslide. The upper slide has an area of 2,700 m2 with a head scarp of 5-7 m and a volume of 15,700 m3. The lower earth flow has an area of 2300 m2, a head scarp of 15 m, and a volume of 287,500 m3. Analysis of aerial photographs indicates the lower slide probably originated between 1956 and 1963. The landslide is located at a geologic unit contact of Eocene deep marine sedimentary rock and intrusive volcanic rock. The landslide was instrumented with 20 crack monitors established across ground cracks and measured periodically. Field measurements did not detect ground crack displacement over a 15 month period. Soil samples indicate the soil is an MH soil with a unit weight of 12 kN/m3 and residual friction angle of 28φ'r which were both used as input for slope stability modeling. Differential DEMs from lidar data were calculated to generate a DEM of Difference (DoD) raster to identify and quantify elevation changes. Historical aerial photograph review, differential lidar analysis, and precipitation data suggest the upper portion of the landslide failed as a result of the December 2007 storm.

Se presenta un procedimiento para evaluar de forma cuantitativa el riesgo por deslizamientos teniendo en cuenta la peligrosidad, los elementos expuestos y su vulnerabilidad. El método utiliza los modelos de susceptibilidad obtenidos previamente a partir de las relaciones estadísticas existentes entre los deslizamientos ocurridos en el pasado (últimos 50 años) y una serie de parámetros del terreno relacionados con la inestabilidad. La frecuencia de deslizamientos en el pasado se ha utilizado para estimar frecuencias futuras. También se ha realizado un inventario y cartografía de los elementos afectados por deslizamientos en el pasado, y se han estimado los daños para cada tipo de elemento teniendo en cuenta la magnitud del tipo de deslizamiento analizado. Posteriormente se estimó la vulnerabilidad, que se expresa en valores de 0 a 1, a partir de la comparación entre pérdidas y valor del elemento afectado.La integración de la peligrosidad, vulnerabilidad y valor del elemento ha permitido obtener modelos de riesgo directo por deslizamiento para cada tipo de elemento. Además se han analizado las pérdidas indirectas ocasionadas sobre las actividades económicas por este proceso. El resultado final es un mapa de riesgo donde cada píxel muestra las pérdidas esperables por deslizamientos en los próximos 50 años / A quantitative procedure for landslide risk mapping has been developed considering hazard, vulnerability and exposed elements. The method is based on a susceptibility model previously developed from statistical relationships between past landslides occurred in the study area (last 50 years) and terrain parameters related to instability. Past landslide behaviour has been used to calculate landslide frequency for the future. An inventory of direct damage due to landslides during the study period was carried out and the main elements at risk in the area identified and mapped. Past monetary losses per type of element have been estimated and expressed as an average 'specific loss' for events of a given magnitude (corresponding to a specified scenario). Vulnerability has been assessed by comparing losses with the actual value of the elements affected and expressed as a fraction of that value (0-1).By integrating hazard, vulnerability and monetary value, direct landslide risk ( /pixel) has been computed for each element considered. Indirect losses from the disruption of economic activities due to landsliding have also been assessed. The final result is a risk map combining all losses per pixel for a 50-year period.

Regional landslide risk, as it is most commonly defined, is a product of the following: hazard, vulnerability and exposed population. The first objective of this research project is to estimate the regional landslide hazard level by calculating its probability of slope failure based on maximum slope angles, as estimated using data provided by digital elevation models (DEM). Furthermore, it addresses the impact of DEM resolution on perceived slope angles, using local averaging theory, by comparing the results predicted from DEM datasets of differing resolutions. Although the likelihood that a landslide will occur can be predicted with a hazard assessment model, the extent of the damage inflicted upon a region is a function of vulnerability. This introduces the second objective of this research project: vulnerability assessment. The third and final objective concerns the impact of urbanization and population growth on landslide risk levels.