Predicting future environmental variables within a decentralized framework
Overview: The Zeus Subnet leverages advanced AI models within the Bittensor network to forecast environmental data. This platform is engineered on a decentralized, incentive-driven framework to enhance trustworthiness and stimulate continuous technological advancement. The datasource for this subnet consists of ERA5 reanalysis data from the Climate Data Store (CDS) of the European Union's Earth observation programme (Copernicus). This comprises the largest global environmental dataset to date, containing hourly measurements from 1940 until the present across a multitude of hundreds of variables. Validators can stream data from this data source in real-time, allowing miners to be queried on terrabytes of data.
Purpose: Traditionally, environmental forecasting is achieved through physics-based numerical weather/environmental prediction (NWP). While this allows for very accurate predictions, it is also highly cost-ineffective, requiring large amounts of computing power for a single forecast. Furthermore, predictions are time expensive to obtain, since the simulation process of these NWP algorithms can take multiple hours to finish. Currently, there is a lot of ongoing research into the development of intelligent, data-driven algorithms for environmental prediction. Such algorithms can potentially be much faster, more accurate, at a fraction of the cost and carbon emissions. This subnet incentives the development of novel and groundbreaking architectures for enviromental data prediction. Through the continious evolution of this subnet, we are able to allow miners to tackle increasingly difficult problems over time.