Bayesian Reasoning for Tropical Cyclone Intensity Forecasting and Risk Analysis

Abstract

Improved methods for tropical cyclone (TC) intensity forecasting and risk analysis are the end products of this project leading to the wider use in the mitigation of the devastating impacts of tropical cyclones. Single-disciplined approaches in TC intensity forecasting by meteorologists and in risk analysis by social scientists so far have not seen satisfactory results. This is because of the intrinsically high degree of complexity in both the modelling and the problem domain parts of the project. The current techniques of TC intensity forecasting are limited to linear regression methods, which may not provide the maximum accuracy for the available information. Bayesian inductive inference using the Minimum Message Length (MML) principle (Wallace Freeman 1987) is used to investigate the likelihood