Ontology-Assisted Expert System for Algae Identification With Certainty Factors

Abstract

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.INDEX TERMS Harmful algal bloom, algae identification, ontology, certainty factor, expert system.

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“…Tested on 60 samples of 20 common harmful algal bloom (HAB) species in Lampung Bay and Jakarta Bay, Indonesia, the system achieved an accuracy of 73.33% and high agreement with expert identification on six algae species. This system could be used as an alternative tool for rapid algal identification or as part of an early warning system for HABs [22].…”

Usability and acceptance of crowd-based early warning of harmful algal blooms

“…In contrast, HAB data and visual information are used to validate HAB events and later as data sources for HAB early warning information for the local community as well as regional and national mitigation of HABs. In addition, Algies, an expert system, has also been developed by Setiawan et al (2021) using an ontology of algae to speed up the identification process of algae that causes HABs. Alboom and Algies are expected to provide government, community, researchers, and other stakeholder institutions regarding HAB events in Indonesia and other countries to speed up decision-making in detecting hazard indications and mitigating the effects of HABs.…”