Forest fire detection system using wireless sensor networks and machine learning

Area of Science:

• Environmental Science
• Computer Science
• Engineering

Background:

• Forest fires pose a global threat, impacting ecosystems and human habitats.
• Human activities are a primary cause of forest fires.
• Early detection is vital to mitigate destruction and consequences like climate change.

Purpose of the Study:

• To propose a system for early-stage forest fire detection using a wireless sensor network.
• To enhance detection accuracy with a machine learning regression model.
• To ensure system longevity and reliability in harsh forest environments.

Main Methods:

• Development of a wireless sensor network for forest fire detection.
• Implementation of a machine learning regression model for enhanced accuracy.
• Design of robust sensor nodes and strategic placement for environmental resilience.

Main Results:

• The proposed system effectively detects forest fires at their initial stages.
• The system demonstrated lower latency in alerting compared to existing methods.
• The standalone system, powered by solar and battery, ensures prolonged operation.

Conclusions:

• The developed wireless sensor network system is effective for early forest fire detection.
• Machine learning integration improves detection accuracy and reduces response time.
• The system's design considerations ensure durability and reliability in challenging forest conditions.