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

IF 6 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Journal of Environmental Informatics Pub Date : 2023-01-01 DOI:10.3808/jei.202300500
Ö. Baydaroğlu, S. Yeşilköy, Y. Sermet, I. Demir
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引用次数: 4

Abstract

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.
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面向下一代人工智能应用的水文学本体综述
遥感、地面测量、模型和模拟、社交媒体和众包以及广泛的结构化和非结构化来源产生的大数据需要大量的数据和知识管理工作。在过去的几十年里,信息技术的创新和发展使得数据和知识管理成为可能,因为在过去的几十年里收集和产生了大量的数据。这使得开放的知识网络得以建立,从而在科学研究和商业世界中产生新的想法。为了设计和开发开放的知识网络,本体是必不可少的,因为它们构成了给定知识领域概念化的支柱。系统的文献综述进行了研究涉及本体涉及水文过程和水资源管理。水文学领域的本体论支持对水文循环复杂结构的理解、监测和表征,以及对其过程的预测。它们有助于基于本体的信息和决策支持系统的发展;了解环境和大气现象;气候和水恢复力概念的发展;用人工智能创造教育工具;加强相关网络基础设施建设。本文综述了基于水文过程的本体开发中的关键问题和挑战,以指导下一代人工智能应用的发展。并结合人工智能和水文科学探讨了未来的研究前景。
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来源期刊
Journal of Environmental Informatics
Journal of Environmental Informatics ENVIRONMENTAL SCIENCES-
CiteScore
12.40
自引率
2.90%
发文量
7
审稿时长
24 months
期刊介绍: Journal of Environmental Informatics (JEI) is an international, peer-reviewed, and interdisciplinary publication designed to foster research innovation and discovery on basic science and information technology for addressing various environmental problems. The journal aims to motivate and enhance the integration of science and technology to help develop sustainable solutions that are consensus-oriented, risk-informed, scientifically-based and cost-effective. JEI serves researchers, educators and practitioners who are interested in theoretical and/or applied aspects of environmental science, regardless of disciplinary boundaries. The topics addressed by the journal include: - Planning of energy, environmental and ecological management systems - Simulation, optimization and Environmental decision support - Environmental geomatics - GIS, RS and other spatial information technologies - Informatics for environmental chemistry and biochemistry - Environmental applications of functional materials - Environmental phenomena at atomic, molecular and macromolecular scales - Modeling of chemical, biological and environmental processes - Modeling of biotechnological systems for enhanced pollution mitigation - Computer graphics and visualization for environmental decision support - Artificial intelligence and expert systems for environmental applications - Environmental statistics and risk analysis - Climate modeling, downscaling, impact assessment, and adaptation planning - Other areas of environmental systems science and information technology.
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