The transfer of energy from solar wind to Earth's magnetic field can cause massive geomagnetic storms, wreaking havoc on key infrastructure systems like GPS, satellite communication, and electric power transmission. The severity of these geomagnetic storms is measured by the Disturbance Storm-time Index, or Dst. 

The goal of the MagNet: Model the Geomagnetic Field challenge was to develop models for forecasting Dst that 1) push the boundary of predictive performance, 2) under operationally viable constraints, and 3) using specified real-time solar-wind data feeds. This is a hard problem where the best approaches are not evident at the outset. Competitors were tasked with improving forecasts both for the current Dst value (t0) and Dst one hour in the future (t1).

Over the course of the competition, DrivenData saw over 600 participants and an impressive 1,200 submissions. The number of submissions is especially notable given the technical constraints of the code execution environment and the limit of 3 submissions per week.

Among the winners, we saw a variety of creative solutions. Competitors used a combination of Long Short-term Memory (LSTM), Gated Recurrent Units (GRU), Convolution Neural Networks (CNN), and Light Gradient-boosted Models (LGBM) to secure the top leaderboard positions. In addition to using different models, competitors experimented with various time windows and imputation methods to deal with sensor malfunctions and missing data.

The top four prize-winners were able to achieve 11.1 - 11.5 nT RMSE on the private test set, beating the benchmark of 15.2 nT. Interestingly, an ensemble of the top four models does best of all with an RMSE of 10.6 nT, achieving a 30% reduction from the benchmark!

Ammar Ali, 1st Place Prediction

  • Prize: $15,000
  • Hometown: Jableh, Syria
  • Username: Ammarali32

Belinda Trotta, 2nd Place

Yanick Medina and Hamlet Medina, 3rd Place

Learn more and meet the winners on DrivenData's challenge page -->