Machine Learning & Weather
Use machine learning to assess historical and future weather patterns across 18 weather stations in Europe, with a focus on finding new trends, predicting future climate scenarios, and identifying safe living regions.
Data Sources:
Historical weather data (last 100+ years) – provided by the european climate assessment & data set project (data set)
Additional needs:
•More weather data from additional stations
•Detailed population and infrastructure data for safe region assessment
•Environmental data (pollution, greenhouse gases)
•Socioeconomic data for understanding the human impact of weather patterns
•Satellite and climate data for granular weather trends
Machine Learning Models Used:
Random Forest
•What it does: Think of this as asking a group of experts for their opinions to make a decision. Each expert (or "decision tree") looks at the weather data in a different way and gives their answer. The "forest" then combines these answers to make a more accurate prediction.
•Why it’s useful: This model helps us figure out which weather factors (like temperature, humidity, etc.) are most important when predicting changes in weather patterns.
Convolutional Neural Networks (CNNs)
•What it does: CNNs are like having a superpower to detect patterns in images. If we think of weather data as layers of images, CNNs help us see hidden patterns in the data, such as how different regions of Europe have experienced changes over time.
•Why It’s Useful: It helps us pinpoint where the weather is behaving unusually, which can alert us to potential climate risks.
Recurrent Neural Networks (RNNs) – LTSMs
•What it does: RNNs are like having a memory that can remember past events to predict the future. Imagine keeping track of weather over time like a diary – RNNs help us understand how weather has been changing and predict what might happen next.
•Why it’s useful: This model helps us spot trends over time, like if extreme weather events are becoming more frequent.
Generative Adversarial Networks (GANs)
•What it does: GANs work by having two systems compete with each other. One system tries to create fake weather data that looks like the real thing, while the other tries to spot the fake data. Over time, this helps us create more realistic predictions about future weather.
•Why It’s Useful: This model lets us simulate possible future weather scenarios based on what we’ve seen in the past, helping us prepare for what might happen in 25-50 years.
Feature Importance Analysis: Random Forest Model
These charts illustrate the most important features that influence weather patterns, derived from our Random Forest model. Precipitation emerged as the most critical factor in classifying pleasant weather at the Budapest weather station. This insight allows us to focus predictive efforts on the variables that have the most substantial impact.
Workflow Overview:
Data Preprocessing: I began by cleaning and normalizing historical weather data from multiple European stations.
Model Training: Using Random Forests, I trained the model to predict pleasant versus unpleasant weather based on several factors, including temperature, humidity, and precipitation.
Feature Importance Calculation: After optimizing the model for accuracy, I calculated feature importance scores, identifying precipitation as the key determinant for weather classification.
Validation and Interpretation: With 100% accuracy at this station, the model's predictions were validated, confirming the strong influence of precipitation on local weather conditions.
This analysis not only provides actionable insights into regional weather patterns but also highlights the scalability of the model for broader use across Europe.
Next Step: Leveraging Deep Learning for Image-Based Weather Classification
Building on feature-based prediction, I expanded to Convolutional Neural Networks (CNNs) for real-time weather pattern recognition. This model automates the identification of conditions like clouds, rain, and sunlight from station images.
Workflow Overview:
Data Collection: 901 images were categorized as cloudy, rain, shine, or sunrise.
CNN Training: The model was trained to classify weather patterns using visual features.
Model Performance:
The confusion matrix shows an 82% accuracy, with high precision in predicting sunrise and rain. Misclassifications occurred mostly between cloudy and shine categories.
The second image highlights a correct, high-confidence prediction of rain, demonstrating the model’s reliability for weather monitoring.
Classification Results: This gallery showcases the CNN’s classifications, revealing the model’s potential for real-time weather forecasting and climate adaptation planning as it continues to be refined with diverse data.
Next Step: Identifying Safe Regions in Europe for the Next 25–50 Years
After weather prediction and classification, I focused on clustering European regions by their projected climate safety profiles using Hierarchical Clustering and k-Nearest Neighbors (kNN).
Workflow Overview:
Data Collection: Historical weather patterns were used to assess climate safety profiles.
Hierarchical Clustering: The dendrogram groups regions with similar climate profiles. Lower branches indicate greater similarity, while higher branches represent distinct climate risks. This visual helps segment regions with favorable conditions, aiding long-term planning and infrastructure development.
kNN Classification: The model predicted new regions' safety profiles based on proximity to established clusters. The confusion matrices show accurate classification for regions like Budapest and Ljubljana.
Key Findings:
Distinct clusters of low-risk regions were identified, particularly in northern and central Europe.
These insights support urban development and population resettlement strategies. As more data is integrated, predictions will become even more precise for identifying climate-safe zones.
Conclusion & Recommendations
This project demonstrates how machine learning techniques can provide crucial insights for predicting future weather patterns and identifying safe regions in Europe amidst climate change. Through feature-based prediction, image classification with CNNs, and clustering of safe regions, this work lays the groundwork for practical applications in climate adaptation, infrastructure development, and long-term planning.
Why It Matters
Accurate Weather Predictions: By leveraging models like Random Forests and CNNs, we can improve the accuracy of weather forecasts, essential for disaster preparedness, agriculture planning, and urban development.
Real-Time Monitoring: The CNN-based image classification can automate weather monitoring systems, enabling governments and organizations to receive timely data on extreme weather events, supporting rapid response efforts.
Planning for Safe Zones: Hierarchical clustering and kNN models offer predictive insights into climate-safe regions. This information is vital for urban planners, policymakers, and investors to make informed decisions about where to allocate resources, develop infrastructure, and resettle populations in anticipation of climate shifts.
Recommendations
Expand Data Collection: Incorporate more diverse weather, socioeconomic, and environmental data to refine predictions and make the models applicable across more regions.
Pilot Testing: Implement pilot programs using these models in high-risk areas to validate their accuracy and integrate real-time data collection for continual refinement.
Collaborate with Stakeholders: Engage governments, urban planners, and environmental agencies to integrate the insights from these models into long-term climate resilience and safety plans.
By continuing to refine these models, this project can drive impactful changes in how we adapt to and mitigate the effects of climate change, offering data-driven solutions for a safer and more resilient future.