Ontology-Assisted Expert System for Algae Identification With Certainty Factors

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, sp...

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Autores principales:	Foni A. Setiawan, Reny Puspasari, Lindung P. Manik, Zaenal Akbar, Yulia A. Kartika, Ika A. Satya, Dadan R. Saleh, Ariani Indrawati, Keiji Suzuki, Hatim Albasri, Masaaki Wada
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Harmful algal bloom algae identification ontology certainty factor expert system Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/cfb93949ebcf45ec883532b32303c8bb
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spelling	oai:doaj.org-article:cfb93949ebcf45ec883532b32303c8bb2021-11-18T00:11:17ZOntology-Assisted Expert System for Algae Identification With Certainty Factors2169-353610.1109/ACCESS.2021.3123562https://doaj.org/article/cfb93949ebcf45ec883532b32303c8bb2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591600/https://doaj.org/toc/2169-3536Harmful 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.Foni A. SetiawanReny PuspasariLindung P. ManikZaenal AkbarYulia A. KartikaIka A. SatyaDadan R. SalehAriani IndrawatiKeiji SuzukiHatim AlbasriMasaaki WadaIEEEarticleHarmful algal bloomalgae identificationontologycertainty factorexpert systemElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147665-147677 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Harmful algal bloom algae identification ontology certainty factor expert system Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Harmful algal bloom algae identification ontology certainty factor expert system Electrical engineering. Electronics. Nuclear engineering TK1-9971 Foni A. Setiawan Reny Puspasari Lindung P. Manik Zaenal Akbar Yulia A. Kartika Ika A. Satya Dadan R. Saleh Ariani Indrawati Keiji Suzuki Hatim Albasri Masaaki Wada Ontology-Assisted Expert System for Algae Identification With Certainty Factors
description	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.
format	article
author	Foni A. Setiawan Reny Puspasari Lindung P. Manik Zaenal Akbar Yulia A. Kartika Ika A. Satya Dadan R. Saleh Ariani Indrawati Keiji Suzuki Hatim Albasri Masaaki Wada
author_facet	Foni A. Setiawan Reny Puspasari Lindung P. Manik Zaenal Akbar Yulia A. Kartika Ika A. Satya Dadan R. Saleh Ariani Indrawati Keiji Suzuki Hatim Albasri Masaaki Wada
author_sort	Foni A. Setiawan
title	Ontology-Assisted Expert System for Algae Identification With Certainty Factors
title_short	Ontology-Assisted Expert System for Algae Identification With Certainty Factors
title_full	Ontology-Assisted Expert System for Algae Identification With Certainty Factors
title_fullStr	Ontology-Assisted Expert System for Algae Identification With Certainty Factors
title_full_unstemmed	Ontology-Assisted Expert System for Algae Identification With Certainty Factors
title_sort	ontology-assisted expert system for algae identification with certainty factors
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/cfb93949ebcf45ec883532b32303c8bb
work_keys_str_mv	AT foniasetiawan ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT renypuspasari ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT lindungpmanik ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT zaenalakbar ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT yuliaakartika ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT ikaasatya ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT dadanrsaleh ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT arianiindrawati ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT keijisuzuki ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT hatimalbasri ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors AT masaakiwada ontologyassistedexpertsystemforalgaeidentificationwithcertaintyfactors
_version_	1718425191934066688

Ontology-Assisted Expert System for Algae Identification With Certainty Factors

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