Evolving General Regression Neural Networks for Tsunami Detection and Response

Abstract

In this paper we propose a system that uses a sensor network to detect and respond to tsunamis. Sensor nodes sense underwater pressure data and send it to commander nodes where it is analyzed. Commander nodes use a general regression neural network (GRNN) to predict which barriers need to be fired in order to lessen the impact of the tsunami. We have implemented two versions of a GRNN to perform prediction and a genetic algorithm to optimize the parameters of the neural network. Finally, we analyze the performance differences for each version with respect to both accuracy and earliness of predictions.

A Sensor Network Architecture for Tsunami Detection and Response

“…In this section, we introduce the GRNN and explain its application to the tsunami prediction problem. We give a brief overview of the experiments and results presented in [2].…”

“…These improvements include a framework for distributed services, a clustering algorithm for flooding efficiency, and a localized route repair mechanism. In addition to these support services, we have also implemented an analysis algorithm [2], which uses a general regression neural network (GRNN) [3] to predict the path of the wave. The GRNN analyzes the pressure data from sensor nodes and predicts which barriers should fire to most effectively impede the tsunami.…”