Wildfire Prediction & Mitigation Detailed To-Do List

Load and inspect the wildfire dataset (e.g., satellite imagery, ground sensor data).
Verify the format of the dataset (CSV, GeoJSON, etc.) and ensure it’s clean.
Handle missing data by either filling or dropping it based on analysis.
Remove duplicates or irrelevant data points.
Identify and handle outliers that may skew results.

Perform an initial analysis using pandas to understand data distribution.
Clean and preprocess geospatial data (e.g., coordinates of wildfire locations, weather stations).
Extract relevant features (e.g., wind speed, temperature, humidity, vegetation type).
Normalize or standardize numerical features where necessary.
Categorize temporal data (dates and times) and convert into usable formats.

Collect and preprocess satellite images (MODIS, VIIRS, or Landsat [CSV]).
Implement image processing techniques to enhance satellite data (e.g., image super-resolution for fire hotspots).
Extract features from imagery (e.g., heatmaps, infrared signatures).
Combine multiple sources of satellite data to improve detection accuracy and reduce latency.
Use machine learning models to classify areas of interest (fire hotspots, smoke plumes).

Gather weather data (wind speed, temperature, humidity) and integrate with fire-related data.
Map geographical features like terrain, vegetation, and water sources.
Process environmental data to calculate fire risk based on historical conditions.
Explore relationships between environmental data and fire occurrence (e.g., dry spells leading to increased fire risk).

Develop machine learning models for early fire detection (e.g., convolutional neural networks for smoke detection).
Train and evaluate models for predicting fire ignition risk based on meteorological data.
Implement real-time smoke detection using UAVs and ground-based sensors.
Test models on historical wildfire datasets and tune for improved accuracy.

Model fire spread using physical-based models (e.g., FARSITE) and machine learning models (e.g., reinforcement learning).
Integrate real-time data to simulate fire spread under various conditions (wind, terrain, humidity).
Build predictive models using environmental inputs to estimate fire growth and direction.
Test fire spread models on historical wildfire data for validation.
Implement dynamic, real-time adjustments to the simulation based on live satellite and sensor data.

Develop an AI-powered resource allocation system for firefighting (e.g., deployment of teams, water drops, and aircraft).
Create evacuation route planning systems using real-time fire data to recommend the safest routes.
Implement multi-agent reinforcement learning (MARL) for optimizing firefighting strategies.
Simulate different firefighting strategies to identify the most efficient ones.

Analyze correlations between wildfire spread and environmental features (wind speed, vegetation, etc.).
Perform statistical analysis to understand how different factors influence fire behavior.
Visualize correlations between satellite imagery, weather patterns, and fire locations.
Build feature importance models to identify the most critical variables in wildfire prediction.

Create a risk assessment model to predict areas with high likelihood of wildfires.
Assess the potential damage by predicting fire impacts on infrastructure, communities, and wildlife.
Develop a spatial model to predict the potential spread of wildfires and their impact on populated areas.

Create visualizations of satellite data to display wildfire hotspots and smoke patterns.
Generate heatmaps and choropleth maps to represent wildfire risk areas across regions.
Design interactive dashboards to display real-time data on wildfire activity and predictions.
Build a reporting system to summarize predictions and wildfire behavior for decision-making authorities.

Deploy early detection models in real-time systems (e.g., using cloud platforms like AWS, Google Cloud).
Set up a monitoring system to track the status of fire outbreaks and model predictions.
Integrate weather data and fire risk models for continuous, adaptive predictions.
Ensure the system can process new satellite data as soon as it becomes available for faster detection.

Validate all models using cross-validation techniques and historical data.
Test the system’s predictions against actual wildfire occurrences and update models accordingly.
Evaluate the system’s scalability and accuracy with large-scale wildfire datasets.
Perform model performance tests to ensure high recall and precision in detecting fires and forecasting their spread.

Analyze the results from the simulation and real-time monitoring systems.
Generate detailed reports on model performance and wildfire predictions.
Collect user feedback (e.g., firefighting teams, government agencies) for improvements.
Implement feedback loops to refine models and prediction systems based on real-world outcomes.