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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