Alberta Wildfire Severity ML Predictor

Inspiration

The increasing frequency and intensity of wildfires, particularly in Alberta, inspired our team to leverage technology in mitigating their devastating impacts. Witnessing the ecological and economic toll these fires have exacted on communities, we were motivated to develop a solution that could provide early warnings and insights into potential wildfire outbreaks and their severity. Our goal was to harness machine learning to not only predict the likelihood of wildfires but also to assess their potential impact, aiding in better preparedness and response strategies.

What it does

The predictor forecasts wildfire occurrences, severity, and impacts in Alberta using data on weather, terrain, and historical wildfires, helping authorities in resource allocation and preventive measures.

How we built it

We aggregated diverse datasets and employed a mix of deep learning and ensemble algorithms to train our model, using Python, TensorFlow, PyTorch, and Scikit-learn for development. We also performed complementary data pre-processing and analysis.

Challenges we ran into

One of the major challenges was the integration and normalization of diverse data sources, each with its unique format and scale. Ensuring data quality and consistency was critical but time-consuming. Another challenge was the inherent unpredictability of wildfires, influenced by numerous, often interdependent factors, making modeling particularly complex. Balancing model complexity with interpretability and computational efficiency also posed significant hurdles.

Accomplishments that we're proud of

We're proud of creating an accurate predictive tool that combines various data types and has received positive feedback from fire management authorities for its real-world applicability.

What we learned

This project deepened our understanding of machine learning's potential and limitations in addressing complex environmental challenges. We learned the importance of interdisciplinary collaboration, combining expertise in data science, environmental science, and emergency management to create a more effective solution. The project also highlighted the critical role of data quality and the innovative ways in which AI can contribute to disaster preparedness and response.

What's next for Alberta Wildfire Severity ML Predictor

We plan to incorporate real-time data for dynamic predictions, expand geographic coverage, integrate with emergency systems, and develop an accessible interface for broader use.

Built With

• linearregressionmodel
• python
• pytorch
• scikit-learn
• vscode