Kernel Metadata Declaration — on demand

What the DOI identifies

Cognitive AI for Wildfire Management in Southern California: Challenges and Potentials.

10.46243/jst.2025.v10.i02.pp51-65 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2025-02-24

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 10 · no. 2 · pp. 51–65

Principal agents

• Mohammad Amir Hossain (author → Author) · ORCID 0009-0003-3067-7581
• Taqi Yaseer Rahman (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 3 links, 16 references. Source: Crossref (member 25296), registered 2025-08-26, last deposited 2026-09-15.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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        "value": "The understanding of complex atmospheric phenomena to forecast wildfires with high accuracy has beendramatically transformed with the introduction of cognitive artificial Intelligence. Incorporating state of theart machine learning tools, including deep learning, Bayesian analysis along with decision trees, neuralnetworks, nearly all information from satellites and data collected in the past has been put into thesesystems. These systems permit cognitive AI to provide unparalleled forecasting that is granular and temporalaccurate thanks to its pattern recognition and real time variable adjustments capabilities.Research case studies covering the 2018 Camp Fire, the Bobcat Fire in 2020, and the Dixie Fire of 2021,have all supported AI's ability to predict and prevent the loss of property and lives. Many crucial strategiesincluding evacuation planning, resource deployment, and long-term wildfire prevention strategies inSouthern California have improved cognitive AI implementation. However, the remaining challenges are dataquality and availability issues, integration with existing management systems, and ethical considerationssurrounding of AI decision-making. This research focuses on fire behaviour simulations, increasing datafusion techniques, and. adaptive learning models. Integration of cognitive AI model with evolving technologieslike drones, IoT sensors, and edge computing holds a magnificent potentials for creating a more efficient andresponsive wildfire management ecosystem. Unsurprisingly, problems with AI technology remain, such asthe need for system integration, data inconsistencies, and ethical issues that AI decisions bring. AI decisionspose a mix of issues that are deeply analytical and calculative.",
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            "unstructured": "A. Islam, F. Masud, M. Ahmed, A. Jafar, J. Ullah, S. Islam, S. Shatabda and A. Islam, “An Attention-Guided Deep-Learning-Based Network with Bayesian Optimization for Forest Fire Classification and Localization,” Forests, vol. 14, no. 10, p. 24, 2023"
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            "unstructured": "D. G. S. Z. M. M. K. G. G. Daoping Wang, “Economic footprint of California wildfires,” Nature Sustainability, 2018"
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            "unstructured": "S. P. H. Y. K. J. D. A. M. J. M. O. P. S. S. R. G. R. K. S. C. S. Adriana E. S., “Modelling Human-Fire Interactions: Combining Alternative Perspectives and Approaches,” Frontiers in Environmental Science, vol. 9, 2021"
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            "unstructured": "J. J. Q. Lingli Wang, “A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing,” GEOPHYSICAL RESEARCH LETTERS, vol. 34, no. 20, p. 5, 2017"
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            "unstructured": "P. K. C. G. P. M. M. P. Savvas Papaioannou, “Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response,” Arxiv, vol. 2, p. 8, 2024"
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            "unstructured": "A. P.,. J. M. Nathan Wood a, “Multi-hazard risk analysis for the U.S. Department of the Interior: An integration of expert elicitation, planning priorities, and geospatial analysis,” Sciencedirect, vol. 82, 2022"
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            "unstructured": "A. L. A. U. Stanley Chinedu Okoro, “A Synergistic Approach to Wildfire Prevention and Management Using AI, ML, and 5G Technology in the United States,” Computers and Society, 2024"
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            "doi": "10.1016/j.jenvman.2023.117908",
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            "doi": "10.1109/access.2022.3222805",
            "unstructured": "X. Chen, B. Hopkins, H. Wang, L. O’Neill, F. Afghah and A. Razi, “Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/ IR Image Dataset,” IEEE, vol. 10, no. 2169- 3536, pp. 121301-121317, 17 November 2022"
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            "unstructured": "A. J. M. L. A. L. V. a. J. W. Suellen Hopfer*, “Repeat wildfire and smoke experiences shared by four communities in Southern California: local impacts and community needs,” iopscience, vol. 2, no. 3, 2024"
        },
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            "key": "ref14",
            "unstructured": "N. D. K. Q. T. T. N. Thanh Cong Phan a, “Real-time wildfire detection with semantic explanations,” Sciencedirect, 2022"
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The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

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