  With water quality management processes, identifying and interpreting
relationships between features, such as location and weather variable tuples,
and water quality variables, such as levels of bacteria, is key to gaining
insights and identifying areas where interventions should be made. There is a
need for a search process to identify the locations and types of phenomena that
are influencing water quality and a need to explain how the quality is being
affected and which factors are most relevant. This paper addresses both of
these issues. A process is developed for collecting data for features that
represent a variety of variables over a spatial region and which are used for
training models and inference. An analysis of the performance of the features
is undertaken using the models and Shapley values. Shapley values originated in
cooperative game theory and can be used to aid in the interpretation of machine
learning results. Evaluations are performed using several machine learning
algorithms and water quality data from the Dublin Grand Canal basin.
