Toxic cyanobacterial blooms (CBs) are becoming more frequent globally, posing a threat to
freshwater ecosystems. While making long-range forecasts is overly challenging, predicting imminent CBs is
possible from precise monitoring data of the underlying covariates. It is, however, infeasibly costly to conduct
precise monitoring on a large scale, leaving most lakes unmonitored or only partially monitored. The challenge
is hence to build a predictive model that can use the incomplete, partially-monitored data to make near-future
CB predictions. By using 30 years of monitoring data for 78 water bodies in Alberta, Canada, combined with
data of watershed characteristics (including natural land cover and anthropogenic land use) and meteorological
conditions, we train a Bayesian network that predicts future 2-week CB with an area under the curve (AUC) of
0.83. The only monitoring data that the model needs to reach this level of accuracy are whether the cell count
and Secchi depth are low, medium, or high, which can be estimated by advanced high-resolution imaging
technology or trained local citizens. The model is robust against missing values as in the absence of any
single covariate, it performs with an AUC of at least 0.78. While taking a major step toward reduced-cost, less
data-intensive CB forecasting, our results identify those key covariates that are worth the monitoring investment
for highly accurate predictions.