Wildfire Response and Prediction Challenge

Inspiration

We were inspired by the sponsor challenge proposed by SAP to build a data-driven solution for wildfire response and prediction. With the increasing frequency and intensity of wildfires, we saw an opportunity to leverage machine learning and optimization techniques to help emergency responders make faster, smarter, and more cost-effective decisions. Our goal was to develop a platform that not only predicts wildfire occurrences but also optimizes firefighting resource deployment to minimize damage and operational costs.

What it does

FireLeaf is a wildfire forecasting and reaction platform that:

• Predicts Fire Incidents: Makes wildfire events and real-time environment factors forecasts for predicting fires in an area.
• Optimizes Deployment of Firefighting Resources: Suggests best deployment of firefighting assets for minimizing loss and cost of response.
• Provides Real-Time Information: Displays real-time warnings about fires, danger, and actions advised through an interactive dashboard.
• Geospatial Visualization: Uses mapping tools to map out high-fire risk areas and track current events.

How we built it

1. Data Collection & Preprocessing

   • We have tried to apply a variety of machine algorithms, including Random Forests and Gradient Boosting, for predicting wildfire probability under environmental factors.
   • Hyperparameter tuning and feature selection helped in improving model performance.

2. Wildfire Prediction Model

   • We experimented with various machine learning models, including Random Forests and Gradient Boosting, to predict wildfire likelihood based on environmental conditions.
     
   • Hyperparameter tuning and feature selection helped improve model performance.
     

3. Resource Optimization

   • We designed a system that leverages real-time fires and formulates best-fit dispatch strategies for minimum cost and minimum response times.
   • Linear programming and heuristic algorithms both played a significant role in decision-making.

4. Visualization & Reporting

   • We constructed interactive dashboards for forecasting real-time wildfire danger and suggesting response actions.
     
   • Geospatial mapping techniques accurately mapped out areas with high-fire potential.

Challenges we ran into

• Data Imbalance: Fire days occur infrequently in relation to non-fire days, and therefore, predictive model training is challenging. To address this, we utilized techniques for oversampling and generating synthetic data.
• Real-Time Processing: Real-time processing of streaming data posed infrastructure-related issues. Scalability options included cloud-based methodologies.
• Resource Constraints: Opting for an ideal cost-versus-response-time balancing forced iterative improvement of our algorithms for optimization.

Accomplishments that we're proud of

• Building a Functional Wildfire Prediction Model: Conquering imbalanced datasets and multi-variable environment interactions for successful forecasting.
• Developing an Optimization System for Resource Allocation: Building a system that maximizes cost-effectiveness and quick reaction in cases of urgency.
• Handling Large Datasets: Effectively processing enormous volumes of real-time and historical wildfire information in order to gain actionable insights.
  
• Real-Time Risk Monitoring: Having an information platform through which emergency responders can make fact-based decisions.
• Impact Potential: Having an awareness that such a project can contribute towards enhancing wildfire response techniques and save lives and habitats.

What we learned

Throughout this work, we developed hands-on expertise in:

• Handling large wildfire and environmental datasets.
• Feature engineering for geospatial and time-series data.
• Training machine learning algorithms for wildfire forecasting.
• Optimization methodologies for efficient use of budget.
• The logistics of real-life disaster response.

What's next for FireLeaf

• Improving Prediction Accuracy: Complementing deep neural networks with satellite images for enhancing accuracy in predicting fire danger.
• Scaling to More Regions: Expansion to allow wildfire forecasting and wildfire suppression in other regions with high wildfire activity worldwide.
• Mobile & API Integration: Developing a first responder app and an API for integration with existing emergency management software.
• Enhancing Real-Time Capabilities: Exploration of IoT sensor networks and satellite data for even real-time and even more reliable fire detection.
• Collaborating with Authorities: Partnership with government departments, environmental agencies, and firefighting departments in practicing FireLeaf in real-life settings.

Built With

• dash
• potly
• python
• sklearn