Landslide analysis in Geographic Information Systems
G. L. Sivakumar Babu and M. D. Mukesh Assistant Professor, Senior Research Fellow, Department of Civil Engineering Indina Institute of Science, Bangalore. mukesh@civil.iisc.ernet.in
Abstract
Geographic Information Systems (GIS) have become the promising tool for an effective analysis associated with the study of geologic hazards. GIS is an ideal tool for landslide modeling owing to its versatility in handling a large set of data, providing an efficient environment for analysis and display of results with its powerful set of tools for collecting, storing, retrieving, transforming and displaying spatial data from the real world. This paper demonstrates the ability of the GIS to incorporate the spatially varying data of ground elevation, soil properties, etc. in the engineering analysis of the slope stability. The key factor in landslide hazard mapping is the assessment of risk associated with the failures. Though in general, models are available, better interpretation and understanding of the failures could be derived from them in a GIS environment. A typical landslide area at Mangalore is under investigation for its hazard potential mapping. This paper discusses the analysis of the earthquake and rainfall induced slope failures in GIS and formulates the possible framework. Introduction Landslides constitute one of the major hazards that cause losses in lives and property. Landslides are one of the complex analyses, involving multitude of factors and need to be studied systematically in order to evaluate the hazard. The increasing computer-based tools are found to be useful in the hazard mapping of landslides. One of such significant tools for hazard mapping of landslides is Geographic Information Systems (GIS). A GIS is defined as a powerful set of tools for collecting, storing, retrieving at will, displaying, and transforming spatial data (Burrough and McDonnel, 1998). One of the main advantages of the use of this technology is the possibility of improving hazard occurrence models, by evaluating their results and adjusting the input variables. An important aspect of landslide investigations is the possibilities to store, treat, and analyze spatiotemporal data that are available. GIS and Landslide analysis The unique capability of GIS to work (to capture, store and manage the data) with data referenced by vast spatial or geographic coordinates and its ability to incorporate appropriate engineering models, have caused its proliferating application across the wide sections of engineering, especially in civil engineering where, management of spatial-data is pivotal for the analysis. As the typical landslide analysis demands, collection of numerous data, storage of them and using them in the analysis could be handled well in the GIS environment. Any spatially-distributed data with a geo-reference to real world could be stored as points, lines and polygons (vector model) or as continuous fields (raster data model). Beyond GIS being used as a spatial database, it assists in modelling applications through handling a special form of data that would otherwise be compromised in conventional analysis (Miles et al. 1999). Also, GIS does not only serve as a database for parameter data, rather qualitative and quantitative data can be integrated through spatial relationships rather than through relationships between attributes that may not exist (Frost et al. 1997). The other facilities such as: Query languages and user interfaces permitting rapid modification of parameter values; Convenient and quick updating of model parameters; Overlay function, where multiple maps are either visually or topologically combined and the its potential in visualization of data using the graphic features, that assists the engineer in verifying data and information pertaining to the model and its application; and developing elevation maps and subsequent slope, aspect and hillshade themes (which are useful in the landslide analysis) are worth mentioning, as they are common to many applications of engineering models (Miles et al. 1999). In particular, the ability of GIS to present the data and analysis results in map forms plays a key role in identifying the critical areas (where more rigorous analysis and improved solution is required) by its interactive visualization in a spatially optimized mode.  Fig. 1: Landslide area at Mangalore, Karnataka
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