Exploring patterns in historic earthquake and Tsunami data

Abstract

Gusiakov has compiled a comprehensive database compiled that gives data such as earthquake intensity, geographical location, resulting tsunami intensity etc. There have been various relationships proposed between earthquake and tsunami intensities, including by Gusiakov, who also proposes that regional effects may affect this relationship. Meanwhile, artificial neural networks (ANNs) have become a very powerful way of establishing input-output relationships, though lacking the formality of multiple regression (MR) techniques. Various approaches have been used to minimize the "black box" nature of ANNs, including the use of sensitivity analysis. Adaptive network based fuzzy inference systems (ANFIS) are a newly emerging alternative to ANNs. In this work, Artificial Intelligence (AI) methods (ANN and ANFIS) along with MR analysis were used as tools to explore the patterns in historic earthquake and tsunami data. The accuracy of the three modeling schemes were compared and sensitivity analyses performed. Vulnerability curves have been developed using Monte Carlo simulation that reasonably match survey based curves for the vulnerability of coastal houses to tsunami wave height. This Monte Carlo simulation was used in this work to establish the resulting reduction of vulnerability if proposed strengthening techniques are adopted.