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

The determination of the non-linear behaviour of multivariate dynamic systems often presents a challenging and demanding problem. Slope stability estimation is an engineering problem that involves several parameters. The impact of these parameters on the stability of slopes is investigated through the use of computational tools called neural networks. A number of networks of threshold logic unit were tested, with adjustable weights. The computational method for the training process was a back-propagation learning algorithm. In this paper, the input data for slope stability estimation consist of values of geotechnical and geometrical input parameters. As an output, the network estimates the factor of safety (FS) that can be modelled as a function approximation problem, or the stability status (S) that can be modelled either as a function approximation problem or as a classification model. The performance of the network is measured and the results are compared to those obtained by means of standard analytical methods. Furthermore, the relative importance of the parameters is studied using the method of the partitioning of weights and compared to the results obtained through the use of Index Information Theory.

A study of slope stability prediction using neural networks

In this paper, bivariate statistical analysis modeling was applied and validated to derive a landslide susceptibility map of Peloponnese (Greece) at a regional scale. For this purpose, landslide-conditioning factors such as elevation, slope, aspect, lithology, land cover, mean annual precipitation (MAP) and peak ground acceleration (PGA), and a landslide inventory were analyzed within a GIS environment. A landslide dataset was realized using two main landslide inventories. The landslide statistical index method (LSI) produced a susceptibility map of the study area and the probability level of landslide occurrence was classified in five categories according to the best classification method from three different methods tested. Model performance was checked by an independent validation set of landslide events. The accuracy of the final result was evaluated by receiver operating characteristics (ROC) analysis. The prediction ability was found to be 75.2% indicating an acceptable susceptibility map obtained from the GIS-based bivariate statistical model.

GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece

Computational intelligence tools for the prediction of slope performance

Shallow landslide susceptibility assessment in a semiarid environment — A Quaternary catchment of KwaZulu-Natal, South Africa

In this paper, an adaptive neuro-fuzzy modeling (ANFIS) is applied in order to map landslide susceptibility for a Mediterranean catchment (Peloponnese, Greece). The relationship between landslides and factors influencing their occurrence is investigated in GIS environment. Seven conditioning factors, including elevation, slope angle, profile curvature, stream density, distance to main roads, geology, and vegetation were considered in the analysis. Six ANFIS models with different membership functions were developed to generate the corresponding landslide susceptibility maps. The outputs, representing the probability level of landslide occurrence, were grouped into five classes. They were then evaluated using an independent dataset of landslide events in two different validation methods: receiver operating characteristics (ROC) analysis and success and prediction rates. The majority of the calculated area under the curve values for the two validation methods was in the range 0.70–0.90 indicating between fair and very good prediction accuracy for the six models. These values also showed that the prediction accuracy depends on the membership functions examined in the ANFIS modeling. Among these functions, the difference of two sigmoidally shaped (Dsigmf) and product of two sigmoidally shaped (Psigmf) presented the highest prediction accuracy.

Maria Ferentinou

Papers

A study of slope stability prediction using neural networks

GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece

Computational intelligence tools for the prediction of slope performance

Shallow landslide susceptibility assessment in a semiarid environment — A Quaternary catchment of KwaZulu-Natal, South Africa

Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area