Mechanistic models are often employed to simulate processes in
coastal environments. However, these predictive tools are highly
specialized, involve certain assumptions and limitations, and can
be manipulated only by experienced engineers who have a thorough
understanding of the underlying principles. This results in
significant constraints on their manipulation as well as large gaps
in understanding and expectations between the developers and users
of a model. Recent advancements in soft computing technologies make
it possible to integrate machine learning capabilities into
numerical modelling systems in order to bridge the gaps and lessen
the demands on human experts. This book reviews the
state-of-the-art in conventional coastal modelling as well as in
the increasingly popular integration of various artificial
intelligence technologies into coastal modelling. Conventional
hydrodynamic and water quality modelling techniques comprise finite
difference and finite element methods. The novel algorithms and
methods include knowledge-based systems, genetic algorithms,
artificial neural networks, and fuzzy inference systems. Different
soft computing methods contribute towards accurate and reliable
prediction of coastal processes. Combining these techniques and
harnessing their benefits has the potential to make extremely
powerful modelling tools.
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