Ontology-assisted Expert System for Algae Identification with Certainty Factor

Harmful Algal Bloom (HAB) is one of nature’s responses to nutrient enrichment in aquatic systems and increasingly occurs in coastal waters, such as in Lampung Bay and Jakarta Bay, Indonesia. HABs present environmental and fisheries management challenges due to their unpredictability, spatial coverage, and detrimental health effects to coastal organisms, including humans. Here, we propose an automated algae species identification system assisted and validated by expert judgment. The system uses ontology as guidance to determine the species of algae and certainty factors to indicate the level of confidence of the experts when providing a statement or judgment for a particular object or event under consideration. We tested the system to identify 60 sample data using 51 predetermined algal characteristics. The tests were narrowed down to the 20 most common HAB-causing algae types found in the study sites and compared with identification by the experts. The results showed that the system has successfully identified the test data with an accuracy of 73.33%. The system also has a high agreement (above 79.75%) with the identification performed by the experts on six algae species. Further improvement on the system’s accuracy could facilitate its use as an alternative tool in rapid algal identification or part of an early warning system for HABs.