|
DSpace at CSIR-CBRI Roorkee >
Articles >
Geotechnical Engineering >
Natural Disaster >
Landslide >
Published Articles >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/782
|
| Title: | Combining Neural Network with Fuzzy, Certainty Factor and Likelihood Ratio Concepts for Spatial Prediction of Landslides. |
| Authors: | Kanungo, D P
Sarkar, S
Sharma, Shaifaly |
| Keywords: | Landslide susceptibility zonation
ANN
Fuzzy
Certainty factor
Likelihood ratio
Success rate
2011 |
| Issue Date: | 5-Mar-2012 |
| Abstract: | 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. |
| Description: | Natural Hazards, Vol. 59 (3), pp. 1491-1512. |
| URI: | http://hdl.handle.net/123456789/782 |
| Appears in Collections: | Published Articles
|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
|
