A Synergistic Approach to Wildfire Prevention and Management Using AI,
  ML, and 5G Technology in the United States
Stanley Chinedu Okoro Alexander Lopez Austine Unuriode
Over the past few years, wildfires have become a worldwide environmental
emergency, resulting in substantial harm to natural habitats and playing a part
in the acceleration of climate change. Wildfire management methods involve
prevention, response, and recovery efforts. Despite improvements in detection
techniques, the rising occurrence of wildfires demands creative solutions for
prompt identification and effective control. This research investigates
proactive methods for detecting and handling wildfires in the United States,
utilizing Artificial Intelligence (AI), Machine Learning (ML), and 5G
technology. The specific objective of this research covers proactive detection
and prevention of wildfires using advanced technology; Active monitoring and
mapping with remote sensing and signaling leveraging on 5G technology; and
Advanced response mechanisms to wildfire using drones and IOT devices. This
study was based on secondary data collected from government databases and
analyzed using descriptive statistics. In addition, past publications were
reviewed through content analysis, and narrative synthesis was used to present
the observations from various studies. The results showed that developing new
technology presents an opportunity to detect and manage wildfires proactively.
Utilizing advanced technology could save lives and prevent significant economic
losses caused by wildfires. Various methods, such as AI-enabled remote sensing
and 5G-based active monitoring, can enhance proactive wildfire detection and
management. In addition, super intelligent drones and IOT devices can be used
for safer responses to wildfires. This forms the core of the recommendation to
the fire Management Agencies and the government.