Name(s) and e-mail (s) (please list primary contact first)*: Alec Engell, aengell@nextgenfed.com     Associated Institution/Project name/Group name*: NextGen Federal Systems     Website url(s): nextgenfed.com     Logo(s): Information about your method:     Forecasting method name*: SPRINTS (Space Radiation Intelligence System)     Shorthand unique identifier for your method (methodname_version, e.g. ModelName_1 or ModelName_201201): SPRINTS v0.1     Short description*: SPRINTS integrates preeruptive metadata and forecasts from the MAG4 system with posteruptive metadata in order to produce high fidelity and preeruptive to posteruptive transitional forecasts for solar-driven events, including SEPs. To catalog start and end times of the four solar-driven events, SPRINTS is capable of generating posteruptive forecasts based on automatic detections employed on 30+ years of GOES X-ray and particle data as well as 20+ years of ACE and DSCOVR solar wind data. SPRINTS leverages machine-learning techniques to make predictions including regression and classification methods such as decision trees, random forests, and k- nearest neighbor.     Model Inputs*: Currently, SPRINTS only uses flare metadata including magnitude, integrated flux, decay phase time, rise phase time, helolongitude, and heliolatitude. However, it is architected to be extensible to any dataset. The next data to be included will be type II radio bursts and CME kinematics from the CORIMP and or CACTUS automated catalogs.     Model Outputs*: The model is designed to predict and output any type of SEP parameter including the time-evolution of the SEP event at the 10, 30, 50 and 100 MeV energies. Currently, it only has results for predicting 10 MeV onsets and 10 MeV peak flux.     References: Engell, A. J., Falconer, D. A., Schuh, M., Loomis, J., & Bissett, D. (2017). SPRINTS: A framework for solar-driven event forecasting and research. Space Weather, 15. https://doi.org/10.1002/ 2017SW001660 Further Model Details:     (1)* Is the forecast made continuously (e.g. probability for the next 24 hours, or a time series), or event-triggered (e.g. by a flare or CME). SPRINTS, by working with MAG4, offers a pre-eruptive and post-eruptive SEP forecasting capability. The main results presented within the reference above focuses on the post-eruptive forecasting (flare-triggered) capability of SPRINTS as a stand-alone SEP forecasting model.     (2)* Is the forecast human generated, human generated but model-based, model-based, or other. Model based.     (3)* If model-based: is the model empirical, physics-based or both. Empirical: statistical and machine-learning