AI-Powered Forest Fire Prediction and Early Warning System for Uttarakhand

Challenge Background

Uttarakhand, a state nestled in the Indian Himalayas, boasts a rich tapestry of biodiversity and extensive forest cover. However, this natural heritage is increasingly threatened by the escalating frequency and severity of forest fires. Recent data paints a stark picture of this growing crisis:

• A Surge in Incidents: Forest fire occurrences have witnessed a dramatic surge in Uttarakhand, skyrocketing from 922 incidents in 2002 to a staggering 41,600 incidents in 2019. This alarming trend continues, with 575 incidents already documented between November 1, 2023, and May 13, 2024, affecting nearly 1,438 hectares and incurring significant financial losses.
• Rapid Escalation: The intensity of the problem is further highlighted by the alarming statistic that within the first five days of April 2024 alone, 361 incidents scorched approximately 567 hectares of forest land.
• Human Influence: The human factor plays a significant role in this crisis, with 351 cases registered related to man-made fires, implicating numerous individuals in causing these devastating incidents.
• Limitations of Current Systems: Existing fire detection systems predominantly rely on satellite imagery and manual reporting, which often result in delays in both detection and response, hindering effective fire management.
• These frequent and intense forest fires pose a grave threat to Uttarakhand's delicate ecological balance, endangering its rich biodiversity, local communities, wildlife, and the state's economy. The urgency of the situation demands a more efficient and proactive approach to forest fire management.

The Problem

Leveraging the power of artificial intelligence (AI) presents a promising avenue for enhancing forest fire management. By integrating real-time data from diverse sources, including satellite imagery, weather patterns, and ground sensors, an AI-based system can significantly improve the accuracy and speed of both fire detection and prediction. This proactive approach will enable quicker response times, minimizing the devastating impact of forest fires on Uttarakhand's invaluable natural resources and communities.

Goal of the Project

1. Develop an AI system capable of analyzing Sentinel-2 satellite images and MODIS to create a model that can assess fire risk levels across different regions of Uttarakhand. The model should be able to:
   1. Predict forest fire hotspots in real-time or
   2. Predict wildfire behaviors by estimating and tracking fire spread rates.
2. Utilize machine learning algorithms to predict potential fire outbreaks based on historical data, current weather conditions (temperature, humidity, wind speed), and vegetation types.
3. Create an automated alert mechanism that notifies forest officials and local communities about detected hotspots and predicted fire risks via SMS or mobile applications. Ensure that alerts are timely to facilitate quick response actions by firefighting teams. Integrate existing systems like the Bhuvan portal and the Forest Survey of India’s alerts to provide comprehensive.

Project Timeline

What you'll learn

1. Data Collection: Collection of satellite and GIS data from multiple sources.
2. Data Cleaning and Preprocessing: Cleaning of GIS data coordinates. Segment the images.
3. Data Analysis: Extract insights from the previous year's data and provide insights for dashboards.
4. Building Machine Learning Models: Use CV to build a model and forecast, and compare it with existing models.
5. Building and Hosting the Platform: Deployment of the model.