The book presents machine learning as an approach to building
models that learn from data, and that can be used to complement the
existing modelling practice in aquatic and geotechnical
environments. It provides concepts of learning from data, and
identifies segmentation (clustering), classification, regression
and control as the learning tasks. A unified methodology based on
the concepts of machine learning, information theory and statistics
is presented that can be followed to build models using data as
well as expert knowledge. Several machine learning methods are used
to extract features to build data-driven models in geotechnics. A
set of regression models are built to predict sediment transport
rates and assess harbour sedimentation. Controllers that replicate
the control strategy of model-based optimal controllers of water
systems are built for situations where fast and accurate decisions
are needed. The models built demonstrate excellent performance;
they may complement or even replace the existing models and can be
used in practice. The performance of the models proves the
effectiveness of the methodology and machine learning in general.
General
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