I’d start with a time‑series approach – maybe an ARIMA or a Prophet model to capture seasonal trends in temperature and precipitation. Then layer in a few explanatory variables like floral abundance or pollen counts, and use a random forest or gradient boosting model to pick up nonlinear interactions. If you can get real‑time hive data, a Bayesian hierarchical model could combine the weather priors with hive‑specific observations. Keep the feature set lean so you don’t overfit, and validate with cross‑validation across years. That should give you a solid baseline to tweak as you gather more data.

Sounds good – keep it lean. I’d use a light linear regression or a small tree model with just a few key predictors: daily max temp, rainfall, and a flower bloom index. Run a stepwise selection to drop any variables that don’t improve the fit. Then expose the results in a one‑page dashboard that shows next‑day swarm risk as a probability, with a clear “low / medium / high” flag. That keeps the output straightforward for beekeepers while still grounded in solid data.

Sounds like a plan – I’ll keep the feature set tight, run cross‑validation on historic swarm data, and produce a simple output: a numeric risk score plus a one‑sentence recommendation. That way the beekeepers can act without parsing a spreadsheet, and we still base it on real evidence. Good luck to you too!