AI Forecasting
Last updated
Last updated
Climate data stored on Hedera can provide a robust foundation for AI-powered weather forecasting models like DeepMind's . Here's an explanation of how this integration works and the benefits:
Data Integrity and Transparency: Climate data stored on Hedera is immutable and timestamped, ensuring that the data is accurate, reliable, and has not been tampered with. This provides a trustworthy source for any AI models that use it.
Decentralized Access: By using Hedera, climate data can be accessed in a decentralized manner by multiple stakeholders, including AI systems, researchers, and organizations, without relying on a single data custodian.
Graph Neural Networks (GNNs) in GraphCast: DeepMind’s GraphCast utilizes Graph Neural Networks (GNNs) to model complex weather systems. GNNs excel at processing data that can be represented as graphs, where the nodes and edges represent spatial and temporal relationships between different weather variables.
Integration with Climate Data on Hedera: By pulling climate data from the chain, GraphCast ensures that the data used for model training and inference is:
Tamper-proof: This reduces the risk of model errors caused by inaccurate or manipulated data.
Consistent and Verifiable: The model can trace back the data's source and timestamp, enhancing the model’s reliability and transparency.
Enhanced Model Reliability: Weather forecasting models rely on the quality of the input data. Blockchain ensures that this data is consistent and free from alterations, which is crucial for accurate predictions.
Improved Data Accessibility and Sharing: Hedera facilitates secure and efficient data sharing among weather agencies, researchers, and AI developers. GraphCast can leverage a broad and diverse set of climate data for better performance.
Global Collaboration: Hedera acts as a shared infrastructure for climate data, encouraging collaboration between different organizations. This way, GraphCast can access a wide range of climate observations, improving the model's ability to forecast global weather patterns.
Historical Data Analysis: Since the distributed ledger stores a permanent record of all data entries, GraphCast can analyze historical climate trends to improve its understanding of weather dynamics and refine its predictive capabilities.
Ecosphere's decentralized network of weather station nodes around the world continuously record climate metrics (e.g., temperature, humidity, wind speed) on Hedera topics. GraphCast can access this real-time, verified data to:
Update Forecast Models: Use the most recent and reliable data to update its weather predictions.
Analyze Patterns: Leverage long-term climate data stored on-chain to detect and learn from patterns or anomalies in weather behavior.
Ensure Accountability: If an extreme weather event occurs, stakeholders can audit the forecast model’s inputs, verifying that the data used was accurate and trustworthy.
Disaster Preparedness: Accurate forecasts using DLT-verified data can help governments and organizations better prepare for natural disasters like hurricanes or floods.
Agricultural Planning: Farmers can use reliable weather forecasts to optimize planting and harvesting schedules.
Energy Sector: Renewable energy providers can better predict energy production (e.g., solar or wind) using precise weather data, enhancing grid stability and efficiency.
In summary, integrating Ecospehers climate data stored onchain into DeepMind’s GraphCast can significantly improve the accuracy and reliability of weather forecasts, while also ensuring data transparency and security.
With Hedera's DLT we can:
Track all the elements that were used to train the AI model
Keep version control in case something goes wrong and that lineage of data and version control of the climate LLM is essential to improve accuracy iteratively.
Allow for multiparty source of information
All 3 require DLT because standard database will not provide these features.