To design a novel model for Tsunami Early Warning by integrating the sensor grid and the Global Positioning System and analyzing sea level parameters for improving tsunami forecast models by considering the real time observations. The Tsunami classification model with real-time sensors placed at different locations and at different depths is proposed. Using wavelet baseddenoising scheme artifacts in the sensor values have been excluded. The parameters such as conductivity, salinity, pressure, temperature, and dissolved oxygen are measured and sensed using multisensor grid architecture. This study examines using an Artificial neural network to design pre and post Tsunami classification by means of novel Back Propagation algorithm based on Artificial Neural Networks. In addition to the above generated classifier output, Seismic signal energy is being observed to perform Tsunami Early Warning .We develop a new Wireless Application Protocol for global communication for early warning of Tsunami.
Volume 10 | 13-Special Issue
Pages: 446-452