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

Abstract

Open science presents a new approach for knowledge discovery, dissemination, and integrity. The idea behind open science is to reinforce research activities and create open knowledge networks by exploring, organizing, and sharing scientific data, as well as making research results transparent, open and integrated. Big data derived from remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and wide variety of structured and unstructured sources requires significant efforts for data and knowledge management. Innovations and developments in information technology for the last couple of decades have made data and knowledge management possible for insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that lead to new ideas in scientific research and business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. In this article, a systematic literature review is conducted to examine research involving ontologies related to hydrological processes and water resources management. The hydrologic cycle (water cycle) is a multi-component and multi-process system that is shaped by various dynamic factors. Because all components of the hydrologic cycle interact with one another and water distribution on Earth is not uniform in time and space, modeling the hydrologic cycle and management of hydrologic cycle components are complex and difficult endeavors. 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 necessary ontology-based information and decision support systems, the comprehension of environmental and atmospheric phenomena, the development of climate and water resiliency concepts, the creation of educational tools with artificial intelligence, and the strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide development of next generation artificial intelligence applications. The authors discuss future research prospects in combination with the artificial intelligence and hydroscience.