Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting subsystem which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed events. This forecasting uncertainty decreases the potential reduction of flood risk, but is seldom accounted for in estimates of the be...

We have developed a probabilistic forecasting methodology through a synthesis of belief­ network models and classical time-series analysis. We present the dynamic network model (DNM) and describe methods for con­ structing, refining, and performing inference with this representation of temporal proba­ bilistic knowledge. The DNM representation extends static belief-network models to more genera...

The remarkable results of Foster and Vohra was a starting point for a series of papers which show that any sequence of outcomes can be learned (with no prior knowledge) using some universal randomized forecasting algorithm and forecast-dependent checking rules. We show that for the class of all computationally efficient outcome-forecast-based checking rules, this property is violated. Moreover,...

Recently, it was shown that calibration with an error less than δ > 0 is almost surely guaranteed with a randomized forecasting algorithm, where forecasts are chosen using randomized rounding up to δ of deterministic forecasts. We show that this error can not be improved for a large majority of sequences generated by a probabilistic algorithm: we prove that combining outcomes of coin-tossing an...

Despite the massive popularity of probabilistic (association) football forecasting models, and the relative simplicity of the outcome of such forecasts (they require only three probability values corresponding to home win, draw, and away win) there is no agreed scoring rule to determine their forecast accuracy. Moreover, the various scoring rules used for validation in previous studies are inad...