Evolving General Regression Neural Networks for Tsunami Detection and Response

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.