UNSUPERVISED MACHINE LEARNING TO MANAGE AQUATIC RESOURCES

摘要

Various implementations provide an aquatic conditions optimization management system accesses aquatic sensor data generated by one or more aquatic sensors, identifies a collection of aquatic data that includes data generated by and collected from the aquatic sensor(s), generates a set of cross-correlation matrices based on the collection of aquatic data, executes a set of unsupervised machine learning algorithms using the set of cross-correlation matrices, and determines one or more optimum conditions for one or more aquatic resources based on the executed set of unsupervised machine learning algorithms. The optimum condition(s) may be communicated to one or more individuals and may include one or more corrective actions to improve one or more of the aquatic resources.