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

Abstract

The understanding of complex atmospheric phenomena to forecast wildfires with high accuracy has been
dramatically transformed with the introduction of cognitive artificial Intelligence. Incorporating state of the
art machine learning tools, including deep learning, Bayesian analysis along with decision trees, neural
networks, nearly all information from satellites and data collected in the past has been put into these
systems. These systems permit cognitive AI to provide unparalleled forecasting that is granular and temporal
accurate 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 strategies
including evacuation planning, resource deployment, and long-term wildfire prevention strategies in
Southern California have improved cognitive AI implementation. However, the remaining challenges are data
quality and availability issues, integration with existing management systems, and ethical considerations
surrounding of AI decision-making. This research focuses on fire behaviour simulations, increasing data
fusion techniques, and. adaptive learning models. Integration of cognitive AI model with evolving technologies
like drones, IoT sensors, and edge computing holds a magnificent potentials for creating a more efficient and
responsive wildfire management ecosystem. Unsurprisingly, problems with AI technology remain, such as
the need for system integration, data inconsistencies, and ethical issues that AI decisions bring. AI decisions
pose a mix of issues that are deeply analytical and calculative.