Abstract
An artificial neural network model is presented to derive streamflow precipitation data. It is tested with actual data coming from a nearby river, referred to a basin area of 356 km 2 and a time period of 11 years. A feedforward multilayer perception with linear output has been built to deal with this problem. The dynamics are caught by the filter structure of the input layer. A special study on crossing properties, based on training sample selection,is made to measure the performance of the network for drought analysis. Sample selection leads to increased accuracy within the sample range and degraded performance for points that are clearly out. Predicted number of droughts, average drought length and deficit are compared with the actual data. The results show that very simple neural network models can give fine results.
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