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Climate Impact Visualizer (CIV)

Advanced AI-Driven Environmental Monitoring & Strategic Risk Analytics

🌍 Project Overview

The Climate Impact Visualizer is a high-performance analytical platform designed to bridge the gap between complex meteorological telemetry and actionable environmental intelligence. Developed as a comprehensive resilience framework, the system synthesizes multi-source data to provide real-time monitoring, 30-year longitudinal historical reconstruction, and AI-powered predictive risk assessments for flood, drought, and thermal stress.

🚀 Key Features

1. Geospatial Intelligence Engine

  • Real-Time Overlays: Dynamic Leaflet-based mapping integration providing spatial visualization of precipitation, wind velocity, and temperature gradients.
  • Localized Telemetry: High-precision coordinate-based analysis utilizing reverse-geocoding for granular regional insights.

2. AI Predictive Risk Analytics

  • Sensor Fusion Modeling: Custom-trained algorithms analyze the convergence of soil moisture, atmospheric aridity (VPD), and precipitation patterns.
  • Probabilistic Forecasting: Generates 24-hour risk scores (High/Medium/Low) with associated confidence intervals for flood and drought events.
  • Thermal Vulnerability: Calculates regional heat vulnerability based on peak temperature thresholds and night-time recovery potential.

3. Longitudinal Historical Deep-Dive

  • 30-Year Evolution: Reconstructs climate shifts since 1993 using high-resolution historical re-analysis.
  • Statistical Indices: Implementation of the Standardized Precipitation Index (SPI) to quantify moisture deficits and identify multi-year drought cycles.
  • Trend Identification: Uses linear regression to isolate significant warming trends and precipitation anomalies.

4. Strategic Adaptation Framework

  • Actionable Intelligence: Automated generation of mitigation protocols (Infrastructure Prep, Resource Allocation, and Safety Advisories) based on real-time risk levels.
  • Resilience Reports: Tools for generating regional environmental summaries to assist in urban and agricultural planning.

🛠 Technical Architecture

Frontend Stack

  • React.js: Component-based UI architecture for high-responsiveness.
  • Chart.js / React-Chartjs-2: Advanced data visualization for projection trends and historical climatology.
  • Leaflet & React-Leaflet: Geospatial rendering engine for atmospheric overlays.
  • Bootstrap 5: Professional, mobile-responsive layout and KPI dashboarding.

Data & API Integrations

  • Open-Meteo: Primary source for terrestrial soil moisture and historical re-analysis.
  • OpenWeatherMap: Real-time atmospheric tile server and global weather telemetry.
  • Nominatim (OSM): Advanced geospatial geocoding and reverse-search logic.
  • AQI Services: Real-time monitoring of PM2.5, NO₂, and O₃ concentrations.

📈 Methodology

The system utilizes a Heuristic Risk Weighting approach combined with Linear Regression for trend analysis.

  • Flood Risk: Evaluated via a combination of soil saturation percentage and peak hourly precipitation volume.
  • Drought Risk: Calculated through an Aridity Index incorporating Vapour Pressure Deficit (VPD) and persistent moisture deficits.
  • Heat Stress: Measured against a localized 95th percentile threshold derived from the 30-year historical baseline of the specific coordinate.

📦 Installation & Setup

Prerequisites

  • Node.js (v16.x or higher)
  • npm or yarn
  • API Keys for OpenWeatherMap (Required for map layers)

Environment Configuration

Create a .env file in the frontend directory:

REACT_APP_OWM_API_KEY=your_api_key_here

Launching the Frontend

cd frontend
npm install
npm start

🛡 Disclaimer

This project was developed as a Final Year Project. The predictive models are probabilistic and intended for research and educational purposes. Always consult local emergency management agencies during extreme weather events.