Harnessing AI and Decentralized Networks for Next-Generation Geophysical Forecasting

Artificial Intelligence (AI) is revolutionizing geosciences, aligning perfectly with the themes of session GI2.4 by enabling the analysis of complex, multidimensional datasets and delivering actionable insights at unprecedented scales. This study presents two innovative AI-driven frameworks addressing critical challenges in soil moisture prediction and geomagnetic disturbance forecasting. Both approaches leverage decentralized networks to achieve scalability, foster collaboration, and enhance model performance through continuous refinement by a distributed community of contributors.

For soil moisture prediction, our multi-stream base model integrates data from Sentinel-2 (high-resolution spectral imagery), SMAP L4 (volumetric water content), ERA5 (meteorological variables), and SRTM (elevation data). The model predicts surface and rootzone soil moisture with six-hour lead times, achieving RMSE values of 0.1087 m³/m³ and 0.1183 m³/m³, respectively, across diverse Köppen-Geiger climate zones. By utilizing a decentralized network, contributors perform inference on 100 km² global regions, generating predictions evaluated against SMAP data using Root Mean Square Error (RMSE) and R² metrics. This system ensures robust model performance while addressing the spatial and temporal gaps inherent in traditional observational networks. These advances have significant implications for agriculture, hydrology, and climate modeling, enabling better water resource management, crop planning, and drought mitigation strategies. 

In geomagnetic disturbance forecasting, our GeoMagModel leverages Prophet, a time-series forecasting library, to predict the Disturbance Storm Time (Dst) index, a key indicator of geomagnetic activity. The model achieves an RMSE of 6.37 for December 2024 datasets, effectively capturing both trend shifts and weekly seasonality. The decentralized community enhances predictive accuracy by dynamically integrating historical and real-time Dst data, which is validated by benchmark predictions of the Kyoto World Data Center’s hourly records. This approach provides near-real-time forecasts critical for safeguarding power grids, satellite systems, and other infrastructure vulnerable to space weather events.

By integrating machine learning with decentralized computing and state-of-the-art data sources, these frameworks offer scalable solutions to longstanding challenges in geophysical monitoring. The decentralized network not only improves scalability but also incentivizes the geoscience community to refine baseline models, fostering innovation and enabling systems to outpace state-of-the-art benchmarks. The implications of this work extend beyond immediate applications, paving the way for hybrid models that combine AI-driven predictions with physical process-based simulations. This fusion has the potential to improve understanding and resilience in critical domains such as water resource planning, disaster mitigation, and space weather forecasting. By addressing the limitations of traditional observation systems and delivering actionable insights at scale, these AI-driven frameworks represent a paradigm shift in how we approach and solve complex geoscientific problems.