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[1] The present work reports the development of a nonlinear technique based on artificial neural network (ANN) for prediction of tsunami travel time in the Indian Ocean. The expected times of arrival (ETA) computation involved 250 representative coastal stations encompassing 35 countries. A travel time model is developed using ANN approach. The ANN model uses non-linear regression where a Multi-layer Perceptron (MLP) is used to tackle the non-linearity in the computed ETA. The back-propagation feed forward type network is used for training the system using the resilient back-propagation algorithm. The model demonstrates a high degree of correlation, proving its robustness in development of a real-time tsunami warning system for Indian Ocean.

ABSTRACT:The variability of mixed layer depth (MLD) and barrier layer thickness (BLT) has profound implications on energy exchange processes at the air-sea interface. More important is the role of MLD and BLT in the genesis and intensification of weather systems. The physical and chemical changes that take place within these layers have significance on biological productivity of the oceans. In this study, the monthly evolution of MLD and BLT for Indian Ocean was compared using the state-of-art world ocean atlas (WOA) and a recently developed comprehensive ocean atlas [referred to as new climatology (NC)]. The study area comprises the geographical boundaries encompassing 30 • N to 60 • S and 30 • E to 120 • E. Qualitative skill assessment of these variables demonstrates that NC is in good agreement with recently reported observational and modelling studies. This brings out the fact that MLD and BLT climatology derived from NC is better than that of WOA.KEY WORDS world ocean atlas; new climatology; mixed layer depth; barrier layer thickness; monthly variability

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