A geographic primitive-based Bayesian framework to predict cyclone-induced flooding

Abstract

The effectiveness of managing cyclone-induced floods is highly dependent on how fast reasonably accurate predictions can be made, which is a particularly difficult task given the multitude of highly variable physical factors. Even with supercomputers, collecting and processing vast amounts of data from numerous asynchronous sources makes it challenging to achieve high prediction efficiency. This paper presents a model that combines prior knowledge, including rainfall data statistics and topographical features, with any new precipitation data to generate a probabilistic prediction using Bayesian learning, where the advantages of dataoriented and heuristic modeling are combined. The terrain is..