Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides

The landslide studies can be categorized as pre- and postdisaster studies. The predisaster studies include spatial prediction of potential landslide zones known as landslide susceptibility zonation (LSZ) mapping to identify the areas/locales susceptible to landslide hazard. The LSZ maps provide an assessment of the safety of existing habitations and infrastructural/functional elements and help plan further developmental activities in the hilly regions. Landslides are one of the natural geohazards that affect at least 15% of land area of India. Different types of landslides occur frequently in geodynamical active domains of the Himalayas. In India, various techniques have been developed and adopted for LSZ mapping of different regions. However, the technique for LSZ mapping is not yet standardized. The present research is an attempt in this direction only. In our earlier work (Kanungo et al. 2006 ), a detailed study on conventional, artificial neural network (ANN)- black box-, fuzzy set-based and combined neural and fuzzy weighting techniques for LSZ mapping in Darjeeling Himalayas has been documented. In this paper, other techniques such as combined neural and certainty factor concept along with combined neural and likelihood ratio techniques have been assessed in comparison with combined neural and fuzzy technique for the preparation of LSZ maps of the same study area in parts of Darjeeling Himalayas. It is observed from the present study that the LSZ map produced using combined neural and fuzzy approach appears to be the most accurate one as in this case only 2.3% of the total area is found to be categorized as very high susceptibility zone and contains 30.1% of the existing landslide area. This approach can serve as one of the key objective approaches for spatial prediction of landslide hazards in hilly terrain. Copyright Springer Science+Business Media B.V. 2011