PhD in AI methods for Real-Time Flood Forecasting (Bristol UK)

[Gladys Castillo Jordán]

-----Original Message-----
From: uai-b...@engr.orst.edu [mailto:uai-b...@engr.orst.edu] On Behalf
Of Jonathan Lawry
Sent: quarta-feira, 4 de Junho de 2008 14:16
To: u...@engr.orst.edu
Subject: [UAI] PhD in AI methods for Real-Time Flood Forecasting (Bristol
UK)

PhD Studentship joint between The University of Bristol and The Proudman
Oceanographic Laboratory

Topic: Applying Rule-Based Models to Sea Level Forecasting

Supervisors: Jonathan Lawry (University of Bristol), Kevin Horsburgh
(Proudman Oceanographic Laboratory)

Project Details

Storm surges are the response of the sea surface to the meteorological
forces of wind and atmospheric pressure. They represent an important
component of total sea level and have been the subject of much scientific
investigation. Marine scientists and engineers need to understand the
statistics of surge occurrence and tide-surge interaction over long
timescales in order to provide estimates of extreme sea level for design
purposes. Numerical models of tides and surges (based on hydrodynamic
equations) have a long history in coastal flood warning. Whilst these
models have been very successful, and form the backbone of current
operational forecast procedures, they are inherently limited by
inaccuracies in bathymetry, meteorological forcing and parameterisations of
sub-grid scale processes. There are now real opportunities for alternative,
data-driven methods of surge prediction using artificial intelligence (AI)
and in particular rule-based models (RBMs). RBMs can provide a high-level
linguistic representation of the mapping between input and output variables
in a prediction problem, allowing for more understandable models which give
an insight into important underlying relationships. Such models can also be
extended to incorporate both the fuzzy and probabilistic uncertainty
typically present in hydrology and oceanography applications. This PhD will
allow the student to demonstrate the use of rule-based models to an
important environmental problem.

The student will develop rule-based models combining probabilistic and
fuzzy uncertainty and compare these with both deterministic forecasting
techniques and alternative time series methods. The student will apply and
extend techniques such as the LID3 algorithm for learning probability
estimation trees incorporating fuzzy description labels. The new models
will then be applied to the improvement of tidal forecasts in regions of
extreme tidal range, and to the prediction of storm surges at key sites
around the UK. The models will also be used to examine the principal
physical causes of extreme sea level events, and the logical rules
governing tide-surge interaction. Finally, the project will demonstrate
the possibility of using artificial intelligence for the interpretation of
ensemble forecasts.

The successful applicant will have a good numerical degree in mathematics,
statistics, computer science, engineering or physical science. Some
knowledge of artificial intelligence, probability theory and fuzzy logic
would be an advantage, although full training will be given. The student
will be provided with training in coastal oceanography and numerical
modelling. All necessary data from tide gauges and from the deterministic
numerical models will be supplied by POL and the Met Office.

Funding

This project is funded by the EPSRC Flood Risk Management Consortium. This
position is open to anyone, but non-EU candidates must find alternative
means to fund the difference between UK and overseas tuition fees.

Contact

Please contact either Jonathan Lawry (j.l...@bris.ac.uk) or Kevin
Horsburgh directly for any informal inquires. The Closing date for
applications is 31 August. Application forms are available online. All
applications and references should be sent directly to:

Emma Weeks

Dept. Engineering Maths.

University of Bristol,

Bristol, BS8 1TR, UK
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