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A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Ö. Baydaroğlu1 *, S. Yeşilköy1, Y. Sermet1, and I. Demir1,2,3

  1. IIHR Hydroscience and Engineering, University of Iowa, Iowa City 52242, USA
  2. Civil and Environmental Engineering, University of Iowa, Iowa City 52242, USA
  3. Electrical and Computer Engineering, University of Iowa, Iowa City 52242, USA

*Corresponding author. Tel.: +1(319)335-5237; fax: +1(319)335-5238. E-mail address: (Ö. Baydaroğlu).


Big data generated by remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and a wide range of structured and unstructured sources necessitates significant data and knowledge management efforts. Innovations and developments in information technology over the last couple of decades have made data and knowledge management possible for an insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that led to new ideas in scientific research and the business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. A systematic literature review was conducted to examine research involving ontologies related to hydrological processes and water resource management. Ontologies in the hydrology domain support the comprehension, monitoring, and representation of the hydrologic cycle’s complex structure, as well as the predictions of its processes. They contribute to the development of ontology-based information and decision support systems; understanding of environmental and atmospheric phenomena; development of climate and water resiliency concepts; creation of educational tools with artificial intelligence; and strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide the development of next generation artificial intelligence applications. The study also discusses future research prospects in combination with artificial intelligence and hydroscience.

Keywords: ontology, hydrology, water resources management, knowledge generation, knowledge representation, knowledge networks, knowledge graph

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