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Harikesh Singh; Li-Minn Ang; Dipak Paudyal; Mauricio Acuna; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava – Technology, Knowledge and Learning, 2025
Wildfires pose significant environmental threats in Australia, impacting ecosystems, human lives, and property. This review article provides a comprehensive analysis of various empirical and dynamic wildfire simulators alongside machine learning (ML) techniques employed for wildfire prediction in Australia. The study examines the effectiveness of…
Descriptors: Artificial Intelligence, Computer Software, Computer Simulation, Prediction
Lorenzo Valente; Federico De Lorenzis; Davide Calandra; Fabrizio Lamberti – IEEE Transactions on Learning Technologies, 2025
In recent years, first responders have faced increasing challenges in their operations, highlighting a growing need for specialized and comprehensive training. In particular, the firefighting incident commanders (ICs) are playing a pivotal role, providing directions to field operators and making critical decisions in emergency situations. Over…
Descriptors: Fire Protection, Experiential Learning, Job Training, Computer Simulation
Tianqi Huang; Zhenan Feng; Daniel Paes; Fei Ying; Xilei Zhao; Max Kinateder; E. R. Langer; Ruggiero Lovreglio – Journal of Computer Assisted Learning, 2025
Background: Wildfires have become increasingly frequent and destructive, highlighting the need for more effective public education on safety and preparedness. Gamification, the use of game design elements in non-game contexts, offers a promising strategy to enhance learner engagement and educational effectiveness compared to traditional methods.…
Descriptors: Natural Disasters, Fire Protection, Safety Education, Gamification

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