Unsupervised machine learning to manage aquatic resources

A method, comprising:

• identifying a collection of aquatic data, wherein the collection of data comprises data generated by a plurality of different aquatic sensors and describes a plurality of conditions within a particular aquatic environment;
• generating a set of cross-correlation matrices from the collection of aquatic data;
• providing the set of cross-correlation matrices as an input to one or more unsupervised machine learning algorithms;
• and
• determining one or more optimum conditions for the particular aquatic environment based on a result of the one or more unsupervised machine learning algorithms.

A system comprising:

• a data processor device;
• computer memory;
• and
• an aquatic conditions optimization management system, executable by the data processor device to:
• identify a collection of aquatic data, wherein the collection of data comprises data generated by a plurality of different aquatic sensors and describes a plurality of conditions within a particular aquatic environment;
• generate a set of cross-correlation matrices from the collection of aquatic data;
• provide the set of cross-correlation matrices as an input to one or more unsupervised machine learning algorithms;
• and
• determine one or more optimum conditions for the particular aquatic environment based on a result of the one or more unsupervised machine learning algorithms.