Description:

The Weather-Impact Analysis on City Infrastructure is a data science and visualization platform designed to study how various weather conditions (rain, storms, heatwaves, snow, humidity, wind speed, etc.) affect a city’s roads, bridges, buildings, utilities, and public services.
The system uses historical weather data, infrastructure maintenance records, and sensor feeds to identify patterns, forecast potential damages, and help city planners make informed decisions.

This project is valuable for urban planning departments, municipal corporations, and disaster management agencies to proactively mitigate weather-related risks.

Key Features:

1. Historical Weather & Infrastructure Correlation – Analyzes past weather events and related infrastructure damage reports.

2. Damage Prediction Model – Uses ML algorithms to forecast the likelihood of infrastructure degradation under specific weather patterns.

3. GIS Mapping Dashboard – Shows vulnerable zones on an interactive city map.

4. Real-Time Weather Alerts – Integrates with live weather APIs to warn about potential risk conditions.

5. Maintenance Priority Suggestions – Ranks infrastructure needing urgent repairs before adverse weather.

6. Cost Impact Analysis – Estimates potential repair costs for weather-related damages.

7. Comparative Analytics – Evaluates the resilience of different neighborhoods or structures.

8. Scenario Simulation – Allows planners to test “what-if” situations, like predicting storm damage if no preventive maintenance is done.

Technology Stack:

• Backend: Node.js / Java / PHP (for APIs and data aggregation)

• Frontend: HTML, CSS, Bootstrap, JavaScript (D3.js / Chart.js for visualization, Leaflet.js / Mapbox for GIS mapping)

• Database: MySQL / MongoDB (stores weather records, maintenance logs, infrastructure details)

• Data Science Layer: Python (pandas, NumPy, scikit-learn, XGBoost for prediction, matplotlib for correlation studies)

• APIs: OpenWeatherMap API, World Weather Online, GIS data APIs

• Optional IoT Integration: Smart sensors on bridges, roads, and buildings for structural health monitoring

Example Use Case:

• In a coastal city, historical analysis shows that heavy rainfall above 150 mm in a day increases road pothole formation by 40% within a week.

• When heavy rainfall is forecast, the system sends alerts to municipal engineers to prepare for road inspections.

• The dashboard highlights 10 vulnerable bridges that have historically suffered damage during storms.

• The city prioritizes pre-storm maintenance on those bridges, saving millions in repair costs.