Ontology-Assisted Expert System for Algae Identification With Certainty Factors

Abstract

Harmful algal blooms (HABs) are one of nature’s responses to nutrient enrichment in aquatic systems and increasingly occur 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 on coastal organisms, including humans. Here, an automated algae species identification system assisted and validated by expert judgment was proposed. The system used 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. The system was tested to identify 60 samples 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 experts. The results showed that the system successfully identified the test data with an accuracy of 73.33%. The system also had a high agreement (above 79.75%) with the identification performed by experts on six algae species. Further improvement of 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.

Keywords

• Harmful algal bloom
• algae identification
• ontology
• certainty factor
• expert system