Wildfire (Hackathon Finalist)

• 

  Our Team

Wildfire Team Stonks

Introduction: Wildfires pose a significant threat to lives, infrastructure, and ecosystems, with their frequency and severity intensifying in recent years. Our project aims to address this pressing issue by leveraging advanced data analytics and machine learning techniques to predict and mitigate wildfires with unprecedented accuracy.

Problem Statement: The challenge of predicting and mitigating wildfires is multifaceted. We identified several key problems:

Identifying Vulnerable Regions: Using statistical analysis, we compared different Forward Sortation Area (FSA) regions in Alberta to pinpoint the most vulnerable areas to wildfires.

Understanding Causes: We investigated common reasons for wildfires in these vulnerable regions to better understand the factors contributing to their occurrence and spread.

Assessing Impact on Indigenous Communities: Indigenous populations are often disproportionately affected by wildfires. We assessed the impact of wildfires on these communities, particularly focusing on the most rapidly spreading fires.

Developing Predictive Models: We developed a predictive model using regression analysis to forecast wildfire occurrence and size, aiding in early detection and effective response.

Methodology: Our approach involved a series of meticulous steps:

Data Cleaning: We conducted a thorough data cleaning phase, removing irrelevant or incomplete entries and handling null values effectively.

Feature Engineering: To enhance model performance, we engineered features such as converting dates into integers to extract relevant time intervals and encoding categorical data numerically for better compatibility with machine learning algorithms.

Model Training: After extensive experimentation, we selected Linear Regression as our primary model, assigning weights to input values based on vulnerability assessments to improve prediction accuracy.

K-Means Clustering: We utilized K-means clustering to identify clusters of data points based on their geographical coordinates and temperature, effectively identifying regions most susceptible to wildfires.

Results and Impact: Our model represents a significant advancement in wildfire prediction and mitigation. By identifying vulnerable regions and forecasting wildfire occurrence and size, we empower stakeholders with timely information for proactive measures and effective resource allocation. Furthermore, our approach facilitates targeted interventions to minimize the impact on vulnerable communities and ecosystems.

Future Directions: While our model demonstrates promising results, our work is far from over. We are committed to continual refinement and improvement, incorporating new data and insights to enhance predictive accuracy and effectiveness. Additionally, we aim to collaborate with stakeholders to implement our solution at scale, ensuring widespread adoption and maximum impact in safeguarding communities and natural habitats from the devastating effects of wildfires.

Built With

• colab
• k-meansclustering
• linearregression
• python