Prediction of landslides using ASTER imagery and data mining models

Details Prediction of landslides using ASTER imagery and data mining models Prediction of landslides using ASTER imagery and data mining models

Mar 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012adspr..49..978s&link_type=abstract

Advances in Space Research, Volume 49, Issue 5, p. 978-993.

Computer Science

Performance

Scientific paper

The aim of this study was to identify landslide-related factors using only remotely sensed data and to present landslide susceptibility maps using a geographic information system, data-mining models, an artificial neural network (ANN), and an adaptive neuro-fuzzy interface system (ANFIS). Landslide-related factors were identified in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. The slope, aspect, and curvature of topographic features were calculated from a digital elevation model that was made using the ASTER imagery. Lineaments, land-cover, and normalized difference vegetative index layers were also extracted from the imagery. Landslide-susceptible areas were analyzed and mapped based on occurrence factors using the ANN and ANFIS. The generalized bell-shaped built-in membership function of the ANFIS was applied to landslide susceptibility mapping. Analytical results were validated using landslide test location data. In the validation results, the ANN model showed 80.42% prediction accuracy and the ANFIS model showed 86.55% prediction accuracy. These results suggest that the ANFIS model has a better performance than does the ANN in predicting landslide susceptibility.

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