Title :
A computationally efficient framework for stochastic prediction of flood propagation
Author :
Wijesundera, I. ; Halgamuge, Malka N. ; Nirmalathas, T. ; Nanayakkara, T.
Author_Institution :
Dept. of Infrastruct. Eng., Univ. of Melbourne, Parkville, VIC, Australia
Abstract :
This paper presents a computationally efficient method to forecast floods stochastically. The main purpose of the method is to encapsulate prior knowledge as off-line calculations leading to earliest possible warnings. The computational efficiency is improved through exploiting the stereotypical features of hydrology and its dependence on topography by combining the parallel water-flow processes into parallel calculation through a probability transition matrix. Efficiency is improved further with the use of properties of Markov matrices in the general equation of the model. Extensive simulations on real rainfall data over parts of Queensland, Australia, during January 2012, revealed that this method was capable of improving the calculation efficiency by over 18 times with respect to gradient based calculations.
Keywords :
Markov processes; floods; geographic information systems; geophysics computing; matrix algebra; rain; weather forecasting; ArcView GIS software; Australia; GeoSFM software; Markov matrices; Queensland; computational efficiency; computationally efficient method; digital elevation model; flood forecasting; flood propagation; geographical information system; geospatial stream flow model; parallel calculation; parallel water-flow processes; probability transition matrix; rainfall data; stereotypical hydrology features; stochastic prediction; Data models; Equations; Markov processes; Mathematical model; Numerical models; Predictive models; Surfaces;
Conference_Titel :
Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1976-8
DOI :
10.1109/ICIAFS.2012.6420038
