A knowledge-based landslide susceptibility zonation system

Landslide susceptibility of a region is important for socioeconomic considerations and engineering applications. Thus, an automated system for mapping of landslide susceptibility could be of significant benefit for society. In this paper, a knowledge-based landslide susceptibility zonation (LSZ) system has been proposed. The system consists of input, understanding, expert, and output modules. The input module accepts thematic images of contributing factors for landslides. The understanding module interprets input images to extract relevant information as required by the expert module. The expert module consists of knowledge base and inference strategy to categorize a region into different landslide intensities. Finally the output module provides a LSZ map. It is a pixel-based system and provides output having the scale same as that of the input maps. The system has been tested to prepare a landslide susceptibility map for the Tehri-Garhwal region in India's lower Himalayas, and further validated with studies for two other different regions. The proposed system provides output commensurate with that provided by experts. The categories of hazard zones have a discrepancy as little as 6.2% in high hazard zones and near to 1.5% and 4% in moderate and low hazard zones, respectively. The high hazard zones in the LSZ maps from the proposed system are supersets of that obtained by experts (i.e., the proposed system provides safer LSZ map). Thus, it can be concluded that the proposed system can be used for preparation of LSZ maps. In the future, the methodology may be extended for real time assessment and prediction of landslide hazards.