Environmental Monitoring & Mitigation System

An environmental monitoring system that combines computer vision, predictive modeling, and Generative AI to detect, forecast, and mitigate pollution events at mining operation sites.

The challenge

Open-pit mining operations generate airborne particulate matter that can impact surrounding communities and worker safety. Traditional monitoring relies on sparse sensor networks with significant spatial gaps and delayed reporting. Events can escalate from acceptable to critical levels faster than manual response protocols allow.

Approach

The system operates in three integrated stages:

1. Real-time estimation

24 video camera streams are processed continuously using computer vision models to estimate pollution levels across the operational area. This provides spatial coverage far beyond what point sensors alone can achieve, creating a dense pollution map updated in near real-time.

2. Predictive forecasting

Time-series models generate forecasts at 1-hour and 6-hour horizons, combining current pollution estimates with meteorological data, operational schedules, and historical patterns. This gives operators advance warning to take preventive action before events reach critical thresholds.

3. Intelligent recommendations

A Generative AI module synthesizes the current situation, forecasts, and operational context to produce natural-language recommendations — specific, actionable guidance written in the language that operators and environmental managers actually use. Instead of abstract dashboards, they receive clear instructions adapted to the current operational state.

Results

• 15% reduction in severe environmental alert events
• Shift from reactive to proactive mitigation strategies
• Real-time spatial pollution maps complementing fixed sensor networks
• Operator-friendly natural-language guidance replacing complex dashboard interpretation

Technology stack

• Vision: Deep learning models for video stream analysis
• Prediction: Ensemble time-series models with meteorological integration
• GenAI: Large language models fine-tuned for operational context
• Infrastructure: Streaming pipelines on Azure Databricks

Note: This description reflects the general type and architecture of systems I have built. Specific client details and operational data are omitted.