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|Title:||Flow categorization model for improving forecasting|
|Keywords:||Artificial neural network|
Support vector machine
|Citation:||Sivapragasam, C.,Liong, S.-Y. (2005). Flow categorization model for improving forecasting. Nordic Hydrology 36 (1) : 37-48. ScholarBank@NUS Repository.|
|Abstract:||Prediction of high magnitude flows is of interest in many hydrological applications such as operation of flood control reservoirs, flood forecasting and gated spillways. Of the various types of existing streamflow prediction approaches, data driven models (such as ANN) are increasingly being preferred over the traditional conceptual models due to their simplicity, fast speed and ease of use. For models that consider only historical streamflow data, an attempt has been made to design a robust model over a wide range of streamflow magnitudes. The model inputs are the immediate past streamflow data which generally do not predict the typically high flows well, particularly for large lead times. In this study, the flow range is divided into three regions (low, medium and high flow regions) and the attributes are decided based on the underlying hydrological process of the flow region. A flow forecasting model is applied for each flow region, using only the historical streamflow data as input. The proposed approach is implemented in Tryggevælde Catchment (Denmark) for 1 - and 3-lead days, using the Support Vector Machine (SVM), which yields promising results, particularly for high flows in a 3-lead day model. © IWA Publishing 2005.|
|Source Title:||Nordic Hydrology|
|Appears in Collections:||Staff Publications|
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