Climate change 2014 - Synthesis Report

Published by the American Geophysical Union as part of the Water Resources Monograph Series, Volume 18. Landslides are a constant in shaping our landscape. Whether by large episodic, or smaller chronic, mass movements, our mountains, hills, valleys, rivers, and streams bear evidence of change from landslides. Combined with anthropogenic factors, especially the development and settlement of unstable terrain, landslides (as natural processes) have become natural disasters. This book charts our understanding of landslide processes, prediction methods, and related land use issues. How and where do landslides initiate? What are the human and economic consequences? What hazard assessment and prediction methods are available, and how well do they work? How does land use, from timber harvesting and road building to urban and industrial development, affect landslide distribution in time and space? And what is the effect of land use and climate change on landslides? [Book Synopsis]

Landslides: Processes, Prediction, and Land Use

Environmental hazards: Assessing risk and reducing disaster

Regional landslide susceptibility assessments pose complex problems. To solve these problems, numerous approaches, such as statistical analysis, geotechnical engineering approach, geomorphologic approach and fuzzy logic, have been employed. However, all the available methods for regional landslide susceptibility assessments have some uncertainties due to a lack of knowledge and variability. Minimizing these uncertainties provides realistic approaches. Use of the fuzzy logic approach to produce a landslide susceptibility map of a landslide-prone area in NW Turkey is the main purpose of the present study. For this purpose, the study includes five main stages, these being the preparation of a landslide inventory of the study area, the application of factor analysis, the extraction of fuzzy if-then rules, the use of a geographical information system, and the control of the reliability of the resulting landslide susceptibility map. Slope angle, slope aspect, land use, weathering depth, water conditions and topographical elevation were considered as landslide conditioning factors for the study area. A total of 23 if-then rules was extracted from the field data. Employing these rules, fuzzified index maps representing each parameter were obtained. Finally, combining these maps, the landslide susceptibility map of the area was prepared. When compared with the landslide susceptibility map, the landslides identified in the area were found to be located in the very high- and high-susceptibility zones. As far as the performance of the fuzzy approach for processing is concerned, the images appear to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach

Spaceborne differential synthetic aperture radar interferometry (DInSAR) has already proven its potential for mapping ground deformation phenomena, e.g. volcano dynamics. However, atmospheric disturbances as well as phase decorrelation have prevented hitherto this technique from achieving full operational capability. These drawbacks are overcome by carrying out measurements on a subset of image pixels corresponding to pointwise stable reflectors (Permanent Scatterers, PS) and exploiting long temporal series of interferometric data. Results obtained by processing 55 images acquired by the European Space Agency (ESA) ERS SAR sensors over Southern California show that the PS approach pushes measurement accuracy very close to its theoretical limit (about 1 mm), allowing the description of millimetric deformation phenomena occurring in a complex fault system. A comparison with corresponding displacement time series relative to permanent GPS stations of the Southern California Integrated GPS network (SCIGN) is carried out. Moreover, the pixel-by-pixel character of the PS analysis allows the exploitation of individual phase stable radar targets in low-coherence areas. This makes spaceborne interferometric measurements possible in vegetated areas, as long as a sufficient spatial density of individual isolated man-made structures or exposed rocks is available. The evolution of the Ancona landslide (central Italy) was analysed by processing 61 ERS images acquired in the time span between June 1992 and December 2000. The results have been compared with deformation values detected during optical levelling campaigns ordered by the Municipality of Ancona. The characteristics of PS, GPS and optical levelling surveying are to some extent complementary: a synergistic use of the three techniques could strongly enhance quality and reliability of ground deformation monitoring. D 2002 Elsevier Science B.V. All rights reserved.

Monitoring Landslides and Tectonic Motions with the Permanent Scatterers Technique

Over the last few years, Artificial Neural Networks (ANN) have been applied in geotechnical engineering and have been used to model almost all aspects of geotechnical engineering problems. Self-Organizing Map (SOM) is an ANN method which is used widely due to visualization and clustering capabilities for analyzing high dimensional data. The paper describes the implementation of this method to a high-dimensional database which contains unsaturated soil data on over 6,200 soil samples coming from sources all over the world. The objective is to investigate clustering tendency of the database and to discover classes of similar features. The analysis showed that the database can be arranged into 3 clusters with similar features. There are, also, interesting qualitative conclusions concerning soil properties.

Elaboration of a High-Dimensional Database of Unsaturated Soils Using Artificial Neural Networks

The Significance of Identifying Potential Failure Mechanisms From Conceptual To Design Level for Open Pit Rock Slopes

/pdf/generic-compressional-strength-prediction-model-applicable-13j4xr1a.pdf

Generic compressional strength prediction model applicable to multiple lithologies based on a broad global database

Classification of Trichonis Lake graben (Western Greece) alluvial fans and catchments using geomorphometry and artificial intelligence

The KwaZulu-Natal coastline on the east coast of South Africa is one of the most densely populated coastlines in Africa and has been subjected to human developments over the last 18 years. In recent years, extreme coastal events, due to a rapidly changing climate have caused much damage along the coast and are predicted to increase in intensity and frequency with the rise in eustatic sea-level. Therefore, assessing the coastal susceptibility in KwaZulu-Natal (KZN) attempts to identify the most sensitive locations along the coast to anticipated sea-level rise and related coastal hazards. This study discusses the application of the Coastal Susceptibility Index, which incorporates and ranks six physical variables, namely: geomorphology, coastal slope, historical shoreline change rate, significant wave height, mean tide range, and relative sea-level rise, to calculate the CSI. This information was displayed based on quartiles, indicating sections of the coastline with a very high, high, moderate or low susceptibility. The majority (34.33%) of the studied KZN is ranked as high susceptibility. More than half the coast (56.72%) is characterized as highly and very highly sensitive, primarily due to the susceptible geological landforms, low lying topography, high erosion rates and a highly significant wave height. The study provides a framework for decision-makers to prioritize coastal zones that require enhanced natural resilience and adopt appropriate management strategies within the study area.

Maria Ferentinou

Papers

Elaboration of a High-Dimensional Database of Unsaturated Soils Using Artificial Neural Networks

The Significance of Identifying Potential Failure Mechanisms From Conceptual To Design Level for Open Pit Rock Slopes

Generic compressional strength prediction model applicable to multiple lithologies based on a broad global database

Classification of Trichonis Lake graben (Western Greece) alluvial fans and catchments using geomorphometry and artificial intelligence

A Coastal Susceptibility Index Assessment of KwaZulu-Natal, East Coast of South Africa