Data collection for landslide susceptibility modeling is often an inhibitive activity. This is one reason why for quite some time landslides have been described and modelled on the basis of spatially distributed values of landslide-related attributes. This paper presents landslide susceptibility analysis in the Klang Valley area, Malaysia, using back-propagation artificial neural network model. A landslide inventory map with a total of 398 landslide locations was constructed using the data from various sources. Out of 398 landslide locations, 318 (80%) of the data taken before the year 2004 was used for training the neural network model and the remaining 80 (20%) locations (post-2004 events) were used for the accuracy assessment purpose. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Eleven landslide occurrence related factors were selected as: slope angle, slope aspect, curvature, altitude, distance to roads, distance to rivers, lithology, distance to faults, soil type, landcover and the normalized difference vegetation index value. For calculating the weight of the relative importance of each factor to the landslide occurrence, an artificial neural network method was developed. Each thematic layer's weight was determined by the back-propagation training method and landslide susceptibility indices (LSI) were calculated using the trained back-propagation weights. To assess the factor effects, the weights were calculated three times, using all 11 factors in the first case, then recalculating after removal of those 4 factors that had the smallest weights, and thirdly after removal of the remaining 3 least influential factors. The effect of weights in landslide susceptibility was verified using the landslide location data. It is revealed that all factors have relatively positive effects on the landslide susceptibility maps in the study. The validation results showed sufficient agreement between the computed susceptibility maps and the existing data on landslide areas. The distribution of landslide susceptibility zones derived from ANN shows similar trends as those obtained by applying in GIS-based susceptibility procedures by the same authors (using the frequency ratio and logistic regression method) and indicates that ANN results are better than the earlier method. Among the three cases, the best accuracy (94%) was obtained in the case of the 7 factors weight, whereas 11 factors based weight showed the worst accuracy (91%).

Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling

There is growing demand among stakeholders across public and private institutions for spatially-explicit information regarding vulnerability to climate change at the local scale. However, the challenges associated with mapping the geography of climate change vulnerability are non-trivial, both conceptually and technically, suggesting the need for more critical evaluation of this practice. Here, we review climate change vulnerability mapping in the context of four key questions that are fundamental to assessment design. First, what are the goals of the assessment? A review of published assessments yields a range of objective statements that emphasize problem orientation or decision-making about adaptation actions. Second, how is the assessment of vulnerability framed? Assessments vary with respect to what values are assessed (vulnerability of what) and the underlying determinants of vulnerability that are considered (vulnerability to what). The selected frame ultimately influences perceptions of the primary driving forces of vulnerability as well as preferences regarding management alternatives. Third, what are the technical methods by which an assessment is conducted? The integration of vulnerability determinants into a common map remains an emergent and subjective practice associated with a number of methodological challenges. Fourth, who participates in the assessment and how will it be used to facilitate change? Assessments are often conducted under the auspices of benefiting stakeholders, yet many lack direct engagement with stakeholders. Each of these questions is reviewed in turn by drawing on an illustrative set of 45 vulnerability mapping studies appearing in the literature. A number of pathways for placing vulnerability mapping on a more robust footing are also identified.

Putting vulnerability to climate change on the map: a review of approaches, benefits, and risks

Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope–Evros region, Greece

Flash floods are considered to be one of the worst weather-related natural disasters. They are dangerous because they are sudden and are highly unpredictable following brief spells of heavy rain. Several qualitative methods exist in the literature for the estimations of the risk level of flash flood hazard within a watershed. This paper presents the utilization of remote sensing data such as enhanced Thematic Mapper Plus (ETM+), Shuttle Radar Topography Mission (SRTM), coupled with geological, geomorphological, and field data in a GIS environment for the estimation of the flash flood risk along the Feiran–Katherine road, southern Sinai, Egypt. This road is a vital corridor for the tourists visiting here for religious purposes (St. Katherine monastery) and is subjected to frequent flash floods, causing heavy damage to man-made features. In this paper, morphometric analyses have been used to estimate the flash flood risk levels of sub-watersheds within the Wadi Feiran basin. First, drainage characteristics are captured by a set of parameters relevant to the flash flood risk. Further, comparison between the effectiveness of the sub-basins has been performed in order to understand the active ones. A detailed geomorphological map for the most hazardous sub-basins is presented. In addition, a map identifying sensitive sections is constructed for the Feiran–Katherine road. Finally, the most influenced factors for both flash flood hazard and critical sensitive zones have been discussed. The results of this study can initiate appropriate measures to mitigate the probable hazards in the area.

Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery

. Digital elevation models (DEMs) represent the topography that drives surface flow and are arguably one of the more important data sources for deriving variables used by numerous hydrologic models. A considerable amount of research has been conducted to address uncertainty associated with error in digital elevation models (DEMs) and the propagation of error to derived terrain parameters. This review brings together a discussion of research in fundamental topical areas related to DEM uncertainty that affect the use of DEMs for hydrologic applications. These areas include: (a) DEM error; (b) topographic parameters frequently derived from DEMs and the associated algorithms used to derive these parameters; (c) the influence of DEM scale as imposed by grid cell resolution; (d) DEM interpolation; and (e) terrain surface modification used to generate hydrologically-viable DEM surfaces. Each of these topical areas contributes to DEM uncertainty and may potentially influence results of distributed parameter hydrologic models that rely on DEMs for the derivation of input parameters. The current state of research on methods developed to quantify DEM uncertainty is reviewed. Based on this review, implications of DEM uncertainty and suggestions for the GIS research and user communities are offered.

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Uncertainties associated with digital elevation models for hydrologic applications: a review

Impediments to using GIS for real-time disaster decision support

The research examines the incompatibility between resolution and scale of spatial data in a spatial modelling environment and the resolution and scale of human spatial decision-making. This incompatibility is rarely considered when a geographic information systems (GIS) is applied to natural hazard risk assessment. However, the ultimate success of a risk management project should be assessed in the context of improved decision-making. Decision utility is an emerging theme in GIS literature that focuses on cognitive issues of GIS and human interactions. The research presents a technique for flood risk modelling using GIS and digital elevation models to map relative risk in urban communities. The study is based on Cairns located in far northern Australia. The risk model accounts for uncertainties inherent in the elevation data by adapting an existing error simulation technique. Techniques for making spatial model assumptions and model error explicit to flood risk managers are introduced and evaluated for risk management decision-making.

Examining GIS decision utility for natural hazard risk modelling

This paper describes the application of three different remote sensing technologies for mapping manna gum (Eucalyptus viminalis) in the Mt. Eccles National Park, Victoria, Australia, where extensive over- browsing by the resident koala population has had an adverse affect on vegetation health. Until recently, the only methods available to park managers to assess the spatial distribution of defoliation have been limited to ground survey or expensive aerial surveys. Since the spatial distribution of defoliation caused by koala browsing is relatively heterogeneous and unclustered, medium resolution satellite sensors do not provide an adequate source of information. However, high resolution imagery acquired by the recently launched Ikonos satellite has the potential to provide a cost-effective solution to mapping vegetation health. To achieve the aim of accurately determining the spatial distribution of vegetation health, imagery has been acquired from three different imaging sensors: airborne linescanner, oblique-looking airborne video and the Ikonos system. The airborne linescanner data was acquired in three bands (near infrared, red and green) at a nominal ground resolution of 0.75m. The airborne video data was also acquired in three bands (red, green and blue), but at a higher spatial resolution. Pan-sharpened Ikonos data was acquired in four bands (near infrared, red, green and blue) at a nominal ground resolution of 1m. In order to maximize the possibilities of detecting individual defoliated trees, it was decided in advance that spectral resolution would be sacrificed in favour of spatial resolution, hence the choice of sensors. Consequently, processing of the image data focussed on developing spatial, rather than spectral, algorithms for detecting areas of defoliation.

Andre Zerger

Papers

Impediments to using GIS for real-time disaster decision support

Examining GIS decision utility for natural hazard risk modelling

Investigation of spaceborne and airborne remote sensing technologies for mapping manna gum (eucalyptus viminalis) health in south-eastern australia