AI-Powered Forest Fire Detection System for Early Warning in Bhutan

Background

Bhutan, known for its rich biodiversity and commitment to environmental conservation, faces significant challenges with forest fires, especially during the dry season from November to April. Despite ambitious goals like maintaining 60% forest cover and being carbon-negative, traditional methods such as visual patrols are inadequate in the country’s rugged terrains. Forest fires in Bhutan, caused by lightning strikes, human activities, or accidents, threaten ecosystems, wildlife, and communities. Globally proven AI-powered systems using sensors, drones, and satellites offer promising solutions to detect and manage forest fires effectively.

Objective

• Develop an AI-powered forest fire detection system capable of real-time fire detection.
• Integrate the detection system with communication tools for timely early warnings.
• Create a cost-effective and efficient solution tailored to Bhutan’s needs.
• Educate local communities on forest fire prevention and response.
• Minimize the number of forest fires and their environmental and societal impact in Bhutan.

Approach

The project followed a structured five-phase plan:

1. Data Collection: Leveraged methods like web scraping to gather data on forest fires and related environmental parameters.
2. Data Preprocessing and EDA: Conducted data cleaning and exploratory analysis to identify key fire indicators.
3. Model Building: Designed machine learning algorithms for real-time fire detection, utilizing advanced tools for image recognition and sensor data.
4. Web App Development: Created an intuitive web-based interface for monitoring and alerting authorities.
5. Report Preparation: Documented methodologies, findings, and actionable insights to aid stakeholders.

Results and Impact

The AI-powered system successfully enabled early detection of forest fires, significantly reducing response times. Key benefits include:

• Improved Accuracy: Real-time detection with minimal false alarms.
• Enhanced Efficiency: Automated monitoring reduced reliance on manual patrols.
• Environmental and Community Safety: Timely alerts mitigated fire spread, protecting ecosystems and human settlements.
  The project demonstrated the viability of integrating AI in environmental management, serving as a model for other regions with similar challenges.

Future Implications

The success of this project paves the way for:

• Scaling the system across Bhutan and other fire-prone regions.
• Incorporating advanced technologies like predictive analytics for risk forecasting.
• Influencing national policies to prioritize AI-driven environmental solutions.
• Strengthening community engagement through education and participatory programs.

This project highlights the transformative potential of AI in creating sustainable, efficient, and impactful solutions to pressing environmental challenges.