ORFFM: An Ontology-Based Semantic Model of River Flow and Flood Mitigation

Mughal, Muhammad Hussain; Shaikh, Zubair A.; Wagan, Asim Imdad; Khand, Zahid Hussain; Hassan, Saif

doi:10.1109/access.2021.3066255

Cited by 25 publications

(23 citation statements)

References 60 publications

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“…IPFS enabled distributed file storage and sharing of DHT among the stakeholders to leverage distributed but persistent data storage. Another challenge of semantic representation with spatiotemporal context for contextual data knowledge addressed in Ontology for Riverflow and Flood Mitigation(ORFFM) [20] leverage the stakeholders' collaboration through semantic data storage and interpretation of heterogeneous distributed data storage.…”

Section: Related Workmentioning

confidence: 99%

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IPFS and Blockchain Based Reliability and Availability Improvement for Integrated Rivers’ Streamflow Data

et al. 2022

Self Cite

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The streamflow data acquisition with various techniques and dispersing of River's locations demand improvement of reliability and frequency aspects. One of the reliability measurement characteristics is data ownership. The authority sharing data authorizes its quality and is also responsible for the wrong decision triggered by incorrect data. The consensus-based crowdsourcing data contribute to aggregated streamflow records' generation. The aggregated streamflow records are stored on a streamflow ledger, a Hyperledger fabric-based ledger for rivers' streamflow data. In contrast, InterPlanery File System(IPFS) distributed file storage system is helpful for policy documents and distribution scheme storage. Blockchainbased techniques for improvement of data-intensive decision support systems. The distributed river streamflow data measured and shared by distributed gauging officials and stored on the blockchain-based distributed storage system contributes to scalability, transparency, availability, and accessibility of shareable data. All stakeholders require quality data with trust and provenance management through a persistent, linked, and immutable copy of data. This technique resolves the conflict or disagreement on streamflow optimization and flood mitigation decisions. In a nutshell, the blockchain technology with IPFS for offchain large files storage would contribute twofold to irrigation systems and flood mitigation domains. On one side, the streamflow data aggregation has consensus from streamflow assessment for software agents and stakeholders. Secondly, the persistent data copy is shared among distributed stakeholders using IPFS-based content addressed file-sharing protocol for a common operating picture to improve effective collaboration and coordination among managers of irrigation systems and flood mitigation activities.

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Section: Related Workmentioning

confidence: 99%

“…The streamflow data is committed by committing peers of a blockchain network for defined irrigation network and irrigation pathways of ORFFM ontology [20]. Each peer node stores its copy and notifies the consortium network's adjacent nodes via sharing hash of the data.…”

Section: ) Persistencementioning

confidence: 99%

IPFS and Blockchain Based Reliability and Availability Improvement for Integrated Rivers’ Streamflow Data

et al. 2022

Self Cite

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“…In [21], the authors identify the core literature available on flood ontologies and present a review on these ontologies from various perspectives. [22] Proposed the Ontology for River Flow and Flood Mitigation (ORFFM) for semantic knowledge formalization with semantic understandability of irrigation, disaster management, related administrative and agricultural domain concepts. This ontology allows the effective coordination, collaborative response activities leads to reduce the impact of a disaster and improve information representation among stakeholders.…”

Section: The Ontologies For the Management Of Natural Disastersmentioning

confidence: 99%

Decision Making in the Context of Natural Disasters Based on a Geographic Information System and the Internet of Things (IoT)

Aissa¹,

Chalal²,

Daouadji³

et al. 2022

Advances in Decision Making

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Decisions within the company, managers in countries or regions are made by one or more groups of decision-makers. The management of natural disasters involves several specialized decision-makers (experts, firefighters, police officers, drivers,). The aim of the chapter is to improve decision making in the context of natural disasters situation. Zero risk in the world does not exist due to natural phenomena that occur randomly and appear suddenly. It is essential to manage the risks in the situation of natural disasters and also to confront the influence of natural disasters on the phenomenon of Soil-Structure Interaction. For this, this chapter presents a conceptual architecture of a knowledge base to manage the risks of natural disasters remotely by a Geographic Information system (GIS) and embedded systems. This architecture is based on the integration of data via different sources of information (GIS, satellites, electronic sensors and comments from experts). To properly manage this information, this chapter uses the ontology of Soil-Structure Interaction With Agents External (OSSIWAE) in the context of the Internet of Things (IoT). A case study is conducted on a prototype of a model for building a structure three-story for testing the usability of the proposed architecture.

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“…Lopez-Pellicer et al ( 2019) develop an ontology, OntoInnova, that gives understanding of the various elements associated with water management research, development, and innovation exchange and collect spatial water management data. For semantic knowledge formalization, Mughal et al (2021b) develop an ontology for River Flow and Flood Mitigation (ORFFM). They provide a novel strategy for connecting the hierarchies of water-producing sources, water distribution systems, as well as contributing to interoperable data sharing for effective water management and flood disaster response.…”

Section: Water Resources Management Domain Summarymentioning

confidence: 99%

“…Ontology-Based Intelligence System: Water resource management requires an intelligent system that optimizes data utilization, knowledge acquisition, and simulation procedures. Numerous studies of ontology-based intelligence system have been conducted on hydrological monitoring (Wang et al, 2017), including flow (Stephen and Hahmann, 2017), drought (Kaewboonma et al, 2014), flood (Wang et al, 2018) and mitigation (Mughal et al, 2021b), water (Howell et al, 2018) and water resources management at river (Oliva-Felipe et al, 2017;Mughal and Shaikh, 2017) and watershed scale (Salah, 2014;Oprea, 2018;Mughal et al, 2021b;Yi and Zuo, 2021) applications. In 13.5% of selected publications, systems are created with the help of machine-readable and understandable ontologies.…”

Section: Summary and Findingsmentioning

confidence: 99%

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

Baydaroğlu¹,

Yeșilköy²,

Sermet³

et al. 2022

Preprint

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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.

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ORFFM: An Ontology-Based Semantic Model of River Flow and Flood Mitigation

Cited by 25 publications

References 60 publications

IPFS and Blockchain Based Reliability and Availability Improvement for Integrated Rivers’ Streamflow Data

IPFS and Blockchain Based Reliability and Availability Improvement for Integrated Rivers’ Streamflow Data

Decision Making in the Context of Natural Disasters Based on a Geographic Information System and the Internet of Things (IoT)

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

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