🌍 GeoNDC Monitor

AI-Powered Climate Intelligence for Africa

Countries Monitored

Continental Avg

Active Alerts

324K+

Data Points

Real-Time Climate Performance Map

Excellent (80-100)

Good (60-79)

Fair (40-59)

Needs Improvement (0-39)

Click any country marker to explore detailed climate indicators, ML-driven insights, and sector-specific performance

Last Updated

December 2024

Climate Leaders

NO₂ Improvement

Forest Loss Alerts

Flaring Anomalies

Advanced AI Climate Intelligence Framework

Data Ingestion

Multi-source satellite data integration

• Sentinel-5P NO₂

• VIIRS Nightfire

• Hansen Forest Change

• ERA5 Weather Data

Data Processing

Quality control & standardization

• Cloud filtering

• Temporal interpolation

• Spatial harmonization

• Missing data imputation

ML Processing

Advanced machine learning algorithms

• LightGBM regression

• Anomaly detection

• Trend analysis

• Confidence estimation

Score Computation

Composite scoring methodology

• Weighted aggregation

• Target normalization

• Confidence weighting

• Uncertainty quantification

Intelligence Layer

Real-time insights & alerts

• Interactive dashboards

• Automated alerts

• Pattern recognition

• Predictive analytics

Technical Architecture & Performance

99.7%

Data Availability

< 24hrs

Update Latency

±2.1%

Average Uncertainty

Weather-Adjusted NO₂ Analysis

Advanced ML Regression Model

Algorithm: LightGBM with hyperparameter optimization

Features (18): Wind speed/direction, precipitation, temperature, humidity, pressure, cloud cover, boundary layer height, seasonal harmonics

Target: Expected NO₂ tropospheric column density

Output: Weather-adjusted Δ% = (Observed - Expected) / Expected × 100

Model Performance & Validation:

• R² Score: 0.847 (cross-validated)
• RMSE: 1.23 × 10¹⁵ molec/cm²
• Confidence Intervals: Bootstrap 95% CI
• Temporal Coverage: 2019-present
• Spatial Resolution: 7×3.5 km

Real-World Applications:

• Policy impact assessment

• Lockdown effectiveness analysis

• Industrial emission monitoring

• Transport sector evaluation

Advanced Forest Loss Monitoring

Multi-Scale Analysis Pipeline

Primary Data: Hansen/UMD Global Forest Change v1.10

Resolution: 30m Landsat-based annual loss

Processing: Temporal interpolation to monthly estimates

Smoothing: 3-month rolling median with seasonal adjustment

Advanced Processing Features:

• Cloud Masking: Pixel QA filtering
• Change Detection: Continuous Change Detection (CCDC)
• Validation: Ground truth from PRODES (Amazon)
• Uncertainty: ±8.7% accuracy (global average)
• Coverage: All forest types, 2000-present

Ecological Indicators:

• Deforestation rate trends

• Protected area effectiveness

• REDD+ performance tracking

• Biodiversity impact assessment

Gas Flaring Intelligence System

Real-Time Anomaly Detection

Algorithm: Multi-threshold statistical analysis

Method: Z-score with seasonal decomposition

Thresholds: |Z| ≥ 2.0 (anomaly), |Z| ≥ 3.0 (extreme)

Output: Severity classification & temporal patterns

VIIRS-Based Detection System:

• Data Source: VIIRS Nightfire (VNF)
• Detection Logic: Temperature & radiance thresholds
• Temporal Resolution: Daily observations
• Spatial Resolution: 750m nadir
• False Positive Rate: <3% (validated)

Operational Applications:

• Regulatory compliance monitoring

• Environmental impact assessment

• Carbon emission quantification

• Industry best practice benchmarking

Advanced Composite Scoring Methodology

Mathematical Framework

Primary Equation:

Score_c,t = Σ_i w_i × Norm(Indicator_i,c,t, Target_i) × α_i,c,t

Where:

c = country, t = time period, i = indicator

w_i = sector weight, α = confidence factor

Sector Weights (Evidence-Based):

• Power & Transport: 40% (largest emission source)

• Land Use & Forestry: 35% (carbon sink capacity)

• Oil & Gas: 25% (methane & flaring impacts)

Normalization Function:

Non-linear scaling with diminishing returns curve to reflect real-world climate impact relationships

Confidence Weighting (α):

Range: 0.75-0.95, based on data quality, temporal coverage, and validation accuracy

2030 NDC Target Alignment

NO₂ Reduction Target

Power & Transport Sector

-30%

vs 2019 baseline

Forest Loss Reduction

Land Use & Forestry

-80%

vs 2019 baseline

Flaring Reduction

Oil & Gas Sector

-80%

vs 2019 baseline

Model Validation & Uncertainty

• Cross-Validation: 5-fold temporal split

• Ground Truth: National inventory reports

• Bias Correction: Systematic adjustment for known sensor limitations

• Uncertainty Propagation: Monte Carlo simulation (1000 iterations)

🌍 GeoNDC Monitor

Real-Time Climate Performance Map

Climate Leaders

NO₂ Improvement

Forest Loss Alerts

Flaring Anomalies

Advanced AI Climate Intelligence Framework

Data Ingestion

Data Processing

ML Processing

Score Computation

Intelligence Layer

Technical Architecture & Performance

Weather-Adjusted NO₂ Analysis

Advanced ML Regression Model

Model Performance & Validation:

Real-World Applications:

Advanced Forest Loss Monitoring

Multi-Scale Analysis Pipeline

Advanced Processing Features:

Ecological Indicators:

Gas Flaring Intelligence System

Real-Time Anomaly Detection

VIIRS-Based Detection System:

Operational Applications:

Advanced Composite Scoring Methodology

Mathematical Framework

2030 NDC Target Alignment

Model Validation & Uncertainty

Country Name

Power & Transport

Land Use & Forestry

Oil & Gas

Observed vs Expected NO₂

Weather-Adjusted Δ%

Monthly Forest Loss (km²)

3-Month Rolling Median

Flare Count & Brightness

Anomaly Detection

About GeoNDC Monitor v2.0

Advanced Methodologies

Data Sources & Validation

Composite Scoring Framework