Early warning & Air quality monitoring System for natural disasters

• Shubham Banwal

• May 2025

Early Warning & Air Quality Monitoring System for Natural Disaster Risk using Big Data

Problem:

Natural disasters and environmental hazards (e.g., wildfires, pollution spikes) often lack timely, data-driven early warning systems, limiting the ability to take preventive action and protect affected populations.

Approach:

Designed and built a scalable monitoring system that aggregates environmental data, applies analytics to detect risk patterns, and generates early warnings based on air quality and atmospheric indicators.

System Design:

• Multi-source data ingestion (air quality sensors, environmental datasets)
• Data processing and aggregation pipeline
• Big data infrastructure for handling large-scale, continuous inputs
• Analytics layer for trend detection and anomaly identification
• Alerting system for early warning notifications
• Visualization/output interface for monitoring environmental conditions

Key Contributions:

• Developed data pipelines to collect and process large-scale environmental data streams
• Applied analytics to identify patterns and anomalies linked to environmental risks
• Built a system for real-time air quality monitoring and early warning generation
• Designed workflows for transforming raw data into actionable environmental insights
• Focused on scalability to handle high-volume, continuous data inputs
• Ensured reliability and consistency of outputs through testing and validation

Constraints & Tradeoffs:

• Data variability and inconsistency across multiple sources
• Tradeoff between real-time processing and large-scale data handling
• Accuracy limitations due to environmental noise and incomplete datasets
• Need for scalable infrastructure within resource constraints