Natural Science Forum / Earth Science / Oceanography / June 2008

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.lawry@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