Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Book . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Book . 2024
License: CC BY
Data sources: Datacite
ZENODO
Book . 2024
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

ADAPTIVE AND PREDICTIVE SYSTEMS FOR WEATHER CATASTROPHES: Convergence of AI and Augmented Reality

Authors: Oleksii, Kolesnikov;

ADAPTIVE AND PREDICTIVE SYSTEMS FOR WEATHER CATASTROPHES: Convergence of AI and Augmented Reality

Abstract

Extreme weather events are intensifying across every region of the globe, and the gap between what meteorological science can predict and what emergency responders can act upon remains dangerously wide. This monograph addresses that gap by examining how Artificial Intelligence and Augmented Reality, taken together, can transform the way societies forecast, communicate, and respond to weather catastrophes. The work begins with a systematic analysis of 21st-century weather hazards — from tropical cyclones and compound flood events to wildfires and severe heat — and documents the limitations of traditional Numerical Weather Prediction systems in meeting the speed and communication demands of modern emergency management. It then reviews the state of the art in AI weather forecasting, covering deep-learning architectures such as GraphCast, Pangu-Weather, GenCast, and ECMWF's operational AIFS, and assesses their performance on extreme-event benchmarks with particular attention to probabilistic calibration.The central contribution is an original six-level taxonomy of AI–AR integration for catastrophe management, ranging from disconnected parallel tools to fully autonomous coordinated response. Built on this taxonomy, the monograph proposes a conceptual architecture for an AR Situational Awareness Platform (AR-SAP) — a modular system that fuses AI forecast data, hazard impact modelling, and adaptive user-context management to deliver role-specific, spatially grounded decision support to field responders and incident commanders in real time.Additional chapters address adaptive-system properties — online learning, feedback-driven threshold calibration, and participatory sensing — alongside a detailed treatment of equity, governance, and the global digital divide. An original application of motivational calibration theory to AR interface design bridges the gap between interactive-system research and emergency management practice.The monograph draws on case studies from East Africa, Japan, the European Union, Ukraine, and the United States, and concludes with a three-phase implementation roadmap, policy recommendations for governments and international organisations, and a forward-looking research agenda. Supplementary annexes cover ethical dimensions, an implementation roadmap, and a glossary of key terms.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average