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Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/791
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| Title: | A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. |
| Authors: | Kanungo, D P
Arora, M K
Sarkar, S
Gupta, R P |
| Keywords: | Landslide susceptibility zonation
GIS
Remote sensing
ANN
Fuzzy
Combined neural and fuzzy
2006 |
| Issue Date: | 5-Mar-2012 |
| Abstract: | Landslides are one of the most destructive phenomena of nature that cause damage to both property and life every year, and
therefore, landslide susceptibility zonation (LSZ) is necessary for planning future developmental activities. In this paper, apart from
conventional weighting system, objective weight assignment procedures based on techniques such as artificial neural network
(ANN), fuzzy set theory and combined neural and fuzzy set theory have been assessed for preparation of LSZ maps in a part of the
Darjeeling Himalayas. Relevant thematic layers pertaining to the causative factors have been generated using remote sensing data,
field surveys and Geographic Information System (GIS) tools. In conventional weighting system, weights and ratings to the
causative factors and their categories are assigned based on the experience and knowledge of experts about the subject and the
study area to prepare the LSZ map (designated here as Map I). In the context of objective weight assignments, initially the ANN as
the black box approach has been used to directly produce an LSZ map (Map II). In this approach, however, the weights assigned
are hidden to the analyst. Next, the fuzzy set theory has then been implemented to determine the membership values for each
category of the thematic layer using the cosine amplitude method (similarity method). These memberships are used as ratings for
each category of the thematic layer. Assuming weights of each thematic layer as one (or constant), these ratings of the categories
are used for the generation of another LSZ map (Map III). Subsequently, a novel weight assignment procedure based on ANN is
implemented to assign the weights to each thematic layer objectively. Finally, weights of each thematic layer are combined with
fuzzy set derived ratings to produce another LSZ map (Map IV). The maps I–IV have been evaluated statistically based on field
data of existing landslides. Amongst all the procedures, the LSZ map based on combined neural and fuzzy weighting (i.e., Map IV)
has been found to be significantly better than others, 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. |
| Description: | Engineering Geology, Vol.85, pp.347-366. |
| URI: | http://hdl.handle.net/123456789/791 |
| Appears in Collections: | Published Articles
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