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The use of data-driven models may be an important alternative in several scientific fields, especially when the available data do not allow utilizing physical hydrologic models because these data must be measured in the basin. . This paper explores important aspects of ANN use: initial training conditions, performance assessment, partitioning of the strong seasonal component in short-term samples and ranking results by a weighted score. Sequential partitioning of the sample was shown to be adequate for cases where the data series has a strong seasonal component and short time response. The nonexceeded error was associated with its frequency, giving a measure of performance that is easily understood and which does not depend on the long familiarity required by traditional methods to evaluate results. A weighted score calculated from several indices removed the difficulty of how to reconcile several statistical measures of performance. The need for repeated artificial neural network training using random starting conditions is established, and the ideal number of repetitions to ensure good training was investigated. A straightforward approach to visualization of forecasting errors is presented, and a pseudoextrapolation region at the domain extremes is identified. The methods were explored using the Quaraí river basin, whose specific characteristics include a rapid response to precipitation events. It therefore provides a good test of artificial neural network methods, including the use of rainfall forecasts which, to be combined with existing data resources, required novel methodological approaches.

Artificial Neural Network Methods Applied to Forecasting River Levels