Detection of tsunami-induced changes using generalized improved fuzzy radial basis function neural network

Akansha Mehrotra (), Krishna Singh, M. Nigam and Kirat Pal

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2015, vol. 77, issue 1, 367-381

Abstract: The coastal areas of Japan were hard hit by a magnitude 9.0 earthquake on 11 March 2011. The earthquake triggered a disastrous tsunami over the area which led to massive destruction. In this paper, tsunami-induced changes in Soma, Watari, Natori and Iwanuma areas using Landsat 7 ETM+ and EO-1 ALI images are identified. The proposed method is based on image classification using radial basis function neural network and generalized improved fuzzy partition FCM algorithm. The pre- and post-tsunami images of the area are first classified using a radial basis function neural network. The pre- and post-tsunami images are classified into three classes including water, vegetation and urban and bare land class. The classified images are compared with other to obtain a set of four change classes. These change classes are labelled to obtain a classified change map. The change map reveals that large areas of vegetations and urban land are washed away by the tsunami in all the four cities, Soma, Watari, Natori and Iwanuma. The accuracy assessment of the method shows that the results obtained are quite satisfactory. The method has high overall accuracy and kappa coefficient value. Copyright Springer Science+Business Media Dordrecht 2015

Keywords: Tsunami; Radial basis function; Classification; Remote sensing (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s11069-015-1595-z

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