Currently, AuraCast provides real-time AQI data and AI-powered 24-hour forecasts for Lucknow. To make the platform more interactive and actionable, we can implement a Scenario Simulation feature that allows users to see how AQI would change under hypothetical conditions.
Feature Overview:
Users can adjust factors like traffic volume, industrial emissions, weather conditions, or seasonal events.
The system predicts AQI changes based on these user-defined scenarios using the existing LSTM/GRU predictive model (or an extended multi-feature time-series model).
This enables users and policymakers to visualize the potential impact of interventions and make informed decisions.
Proposed Workflow:
Take the latest input data for a zone (AQI, traffic, emissions, weather, etc.).
Apply user-defined adjustments (e.g., reduce traffic by 30%).
Feed the adjusted input into the predictive model.
Compare predicted AQI with baseline to show impact.
Visualize results on heatmaps, charts, or interactive dashboards.
Benefits:
Helps citizens understand how actions (like traffic reduction) can improve air quality.
Assists policymakers in evaluating impact of proposed interventions before implementation.
Adds a unique interactive feature to AuraCast, making it more than a forecasting app.
Currently, AuraCast provides real-time AQI data and AI-powered 24-hour forecasts for Lucknow. To make the platform more interactive and actionable, we can implement a Scenario Simulation feature that allows users to see how AQI would change under hypothetical conditions.
Feature Overview:
Users can adjust factors like traffic volume, industrial emissions, weather conditions, or seasonal events.
The system predicts AQI changes based on these user-defined scenarios using the existing LSTM/GRU predictive model (or an extended multi-feature time-series model).
This enables users and policymakers to visualize the potential impact of interventions and make informed decisions.
Proposed Workflow:
Take the latest input data for a zone (AQI, traffic, emissions, weather, etc.).
Apply user-defined adjustments (e.g., reduce traffic by 30%).
Feed the adjusted input into the predictive model.
Compare predicted AQI with baseline to show impact.
Visualize results on heatmaps, charts, or interactive dashboards.
Benefits:
Helps citizens understand how actions (like traffic reduction) can improve air quality.
Assists policymakers in evaluating impact of proposed interventions before implementation.
Adds a unique interactive feature to AuraCast, making it more than a forecasting app.