Wildfire Prediction & Mitigation Detailed To-Do List

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

[x] 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.

SanPranav / QcommVNE_Frontend