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 partitioned into geographic primitives (GPs) based on manual inspection of flood propagation vector fields in order to simplify the stochastic system identification. High calculation efficiency is achieved through statistically summarizing simultaneous events spread across geography into primitives, allowing a distributed updating algorithm leading to parallel computing. Markov chain processes identified for each of these GPs, based on both simulation and measured rainfall data, are then used in real-time predictions of water flow probabilities. The model takes a comprehensive approach, which enables flood prediction even before the landfall of a cyclone through modularizing the algorithm into three prediction steps: cyclone path, rainfall probability density distribution, and temporal dynamics of flood density distribution. Results of comparative studies based on real data of two cyclones (Yasi and Tasha) that made landfall in Queensland, Australia, in 2010/11 show that the model is capable of predicting up to 3 h ahead of the official forecast, with a 33% improvement of accuracy compared to the models presently being used.

MFPT calculation for random walks in inhomogeneous networks

“…), it is understood that if it is possible to divide the network into portions where all nodes within that sub-network have (approximately) similar transport properties for MFPT calculations, the complexity of the problem can reduce considerably as MFPT prediction between any two points within such network portion would be straight forward using methods such as that presented in [10]. In our previous work in [35,36] we have presented a method of reducing the calculation complexity for inhomogeneous networks by dividing the problem of predicting flood propagation through identifying 'Geographic Primitives' based on the terrain slope profile.…”

Predicting the mean first passage time (MFPT) to reach any state for a passive dynamic walker with steady state variability

“…Methods that take a Markov chain approach using an eigenmode analysis on the state transition probability matrices (TPM) [9, 16] are limited to providing a global value for reaching the absorbing state (failure state). A global mean first passage time (MFPT) prediction is a valuable tool in optimizing stability through system parameters.…”