EcoLogistics: A Logistics Resilience Analysis System Integrating Meteorological Intelligence and Machine Learning

This article introduces the EcoLogistics project, an end-to-end climate risk analysis system that combines meteorological data, SQL analysis, Power BI dashboards, and machine learning to help logistics enterprises identify vulnerable routes, predict delivery delays, and shift from "post-event remediation" to "pre-event prevention". The project was developed by sneha-65, with source code available on GitHub (link: https://github.com/sneha-65/EcoLogistics-Climate-Vulnerability-Fleet-Resilience-Analysis), and was released on June 12, 2026.

Global climate change has led to frequent extreme weather events, causing billions of dollars in annual losses to the logistics industry. Traditional logistics scheduling passively responds to weather and lacks proactive risk assessment. EcoLogistics aims to address this pain point by building a climate risk analysis system to help enterprises shift to pre-event prevention.

System Architecture: Four-in-one technology integration

1. Meteorological Intelligence Layer: Accesses real-time/historical meteorological data (precipitation, temperature, wind speed, etc.) and correlates route spatio-temporal information.
2. SQL Analysis Layer: Integrates multi-source data (orders, GPS, meteorology) for feature engineering and complex queries.
3. Power BI Dashboard: Provides real-time risk maps, route vulnerability heatmaps, KPI monitoring, and early warnings.
4. Machine Learning Layer: Predicts delay probability/duration, calculates risk scores, and recommends optimization solutions.

Core Functions:

• Vulnerable Route Identification: Identifies high-risk sections by integrating geographic features, historical weather, traffic flow, etc.
• Severe Delay Prediction: Inputs 72-hour future weather forecast + route + vehicle information, outputs delay probability, duration, and risk level.

Data Pipeline: Data Source Layer → Ingestion Layer → Storage Layer → Processing Layer → Service Layer (integrates meteorological APIs, GPS trajectories, order system data). Model Selection: Gradient Boosting Trees (XGBoost/LightGBM), Random Forest, Time Series Models (ARIMA/Prophet). Evaluation Metrics: RMSE, MAE, Precision/Recall (binary classification), business metrics such as successful early warning rate.

Challenges and Solutions:

1. Data Quality: Multi-source fusion, cleaning pipeline, quality scoring.
2. Real-time Performance: Stream processing architecture, model precomputation, incremental updates.
3. Interpretability: SHAP value analysis, rule extraction, visual explanation. 4.Cold Start: Transfer learning, integration with geographic information, expert knowledge injection.

Value:

• Cost Savings: Reduces emergency scheduling costs, optimizes fuel consumption, lowers claims.
• Customer Satisfaction: Proactively communicates delays, improves on-time delivery rate.
• Operational Efficiency: Dynamically adjusts capacity, optimizes inventory layout.

Application Scenarios:

• Daily Scheduling: Adjusts routes/departure times in advance.
• Emergency Response: Identifies high-risk orders and activates plans during typhoon warnings.
• Long-term Planning: Adds transit warehouses to reduce long-distance transportation risks.

Short-term Expansion: Multi-modal data fusion (traffic cameras, social media, IoT), finer spatio-temporal granularity, multi-objective optimization. Long-term Vision: Build an industry ecosystem, support climate adaptation planning, collaborate with autonomous driving.

Learning Value: End-to-end project example, multi-tech stack integration, domain knowledge combination, interpretable AI practice, real-time system design.