Journal of Global Communication
Year : 2008, Volume : 1, Issue : 1
First page : ( 64) Last page : ( 72)
Print ISSN : 0974-0600.

Coastal disaster warning mechanisms in Tamil Nadu

Aram I. Arul, Assistant Professor

Department of Media Sciences, Anna University Chennai.

Post-Doctoral Fellow from the London School of Economics.

Abstract

There are two types of disasters namely man made and natural. Natural disasters too could be predicted with the latest development in the field of technology. This information must be disseminated to the public so that they may not be caught unawares. Training the community to be prepared for any type of disaster to reduce the human loss is called the Community Based Disaster Preparedness (CBDP). Information has to be collected from different sources and they are to be disseminated to the common people. With the warnings given, the public must be trained as to what to do and what not to do to minimize loss of lives. Effective disaster preparedness requires capacity to detect hazards, monitor information and convert the information into credible, accurate, unambiguous and timely alerts and warnings. Information and Communication Technology (ICT) has been recognized as integral to disaster management and the cornerstone for early warning systems and the backbone of emergency responses to disasters. India has over 7,500 km of coastline, distributed along nine coastal states and four union territories. It has 1.025 million active fishermen. Except for a rare event of tsunami, cyclone is a recurring feature in the Bay of Bengal. Models for mainstreaming of disaster risk management at all levels with focus on district and community level activities are being tried out in some hazard-prone areas in Tamil Nadu so that they can be replicated elsewhere in the country. The vital output of these projects is multi-hazard risk management and sustainable recovery plans at community, panchayat and other administrative levels. This paper examines the effectiveness of pre- and post-tsunami disaster warning models using ICT.

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