Machine-learning paradigms for selecting ecologically
significant input variables
Engineering Applications of Artificial Intelligence, (2007)Viewpoint, Article in Press.DOI: doi:10.1016/j.engappai.2006.11.016
Abstract
Harmful algal blooms, which are considered a serious
environmental problem nowadays, occur in coastal waters
in many parts of the world. They cause acute ecological
damage and ensuing economic losses, due to fish kills
and shellfish poisoning as well as public health
threats posed by toxic blooms. Recently, data-driven
models including machine-learning (ML) techniques have
been employed to mimic dynamics of algal blooms. One of
the most important steps in the application of a ML
technique is the selection of significant model input
variables. In the present paper, we use two extensively
used ML techniques, artificial neural networks (ANN)
and genetic programming (GP) for selecting the
significant input variables. The efficacy of these
techniques is first demonstrated on a test problem with
known dependence and then they are applied to a
real-world case study of water quality data from Tolo
Harbour, Hong Kong. These ML techniques overcome some
of the limitations of the currently used techniques for
input variable selection, a review of which is also
presented. The interpretation of the weights of the
trained ANN and the GP evolved equations demonstrate
their ability to identify the ecologically significant
variables precisely. The significant variables
suggested by the ML techniques also indicate
chlorophyll-a (Chl-a) itself to be the most significant
input in predicting the algal blooms, suggesting an
auto-regressive nature or persistence in the algal
bloom dynamics, which may be related to the long
flushing time in the semi-enclosed coastal waters. The
study also confirms the previous understanding that the
algal blooms in coastal waters of Hong Kong often occur
with a life cycle of the order of 1-2 weeks.
