TL;DR: In this article, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas, manipulated using ArcView GIS.
TL;DR: In this article, the authors developed a GIS-aided urban flood hazard zoning of the two cities applying multicriteria decision analysis and evaluated it by means of uncertainty and sensitivity analysis.
TL;DR: In spite of recent achievements, the use of GIS in the domain of prevention and mitigation of natural catastrophes remains a pioneering activity and the International Decade for Natural Disaster Reduction will probably come to an end without achieving significant advances in the prediction and control of natural disasters.
Abstract: Technologies such as Geographical Information Systems (GIS) have raised great expectations as potential means of coping with natural disasters, including landslides. However, several misconceptions on the potential of GIS are widespread. Prominent among these is the belief that a landslide hazard map obtained by systematic data manipulation within a GIS is assumed to be more objective than a comparable hand-made product derived from the same input data and founded on the same conceptual model. Geographical data can now be handled in a GIS environment by users who are not experts in either GIS or natural hazard process fields. The reality of the successful application of GIS within the landslide hazard domain seems to be somewhat less attractive than current optimistic expectations. In spite of recent achievements, the use of GIS in the domain of prevention and mitigation of natural catastrophes remains a pioneering activity. Diffusion of the technology is still hampered by factors such as the difficulty in acquiring appropriate raw data, the intrinsic complexity of predictive models, the lack of efficient graphical user interfaces, the high cost of digitisation, and the persistence of bottlenecks in hardware capabilities. In addition, researchers are investing more in tuning-up hazard models founded upon existing, often unreliable data than in attempting to initiate long-term projects for the acquisition of new data on the causes of catastrophic events. Governmental institutions are frequently involved in risk reduction projects whose design and implementation appear to be governed more by political issues than by technical ones. There is an unfortunate general tendency to search for data which can be collected at low cost rather than attempting to capture the information which most readily explains the causes of a disaster. If the technical, cultural, economic and political reasons for this unhealthy state cannot be adequately tackled, the International Decade for Natural Disaster Reduction will probably come to an end without achieving significant advances in the prediction and control of natural disasters.
TL;DR: In this article, the authors dealt with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data, and the results of the analysis were verified using the landslide location data and compared with the probabilistic model.
Abstract: This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.
TL;DR: The 2011 Tohoku earthquake illustrates the limitations of earthquake hazard mapping as discussed by the authors, showing that earthquake occurrence is typically more complicated than the models on which hazard maps are based, and that the available history of seismicity is almost always too short to reliably establish the spatiotemporal pattern of large earthquake occurrence.