Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6412418 | Journal of Hydrology | 2014 | 14 Pages |
â¢SOM forms a distinguishable topology of regional flood inundation maps.â¢R-NARX repeatedly adapts the selected map in SOM for nowcasting inundated volumes.â¢SOM-R-NARX nowcasts multi-step-ahead regional inundation maps in typhoon events.â¢Promote spatial inundation forecasts and provide visible regional inundation maps.
SummaryThis study proposes a hybrid SOM-R-NARX methodology for nowcasting multi-step-ahead regional flood inundation maps during typhoon events. The core idea is to form a meaningful topology of inundation maps and then real-time update the selected inundation map according to a forecasted total inundated volume. The methodology includes three major schemes: (1) configuring the self-organizing map (SOM) to categorize a large number of regional inundation maps into a meaningful topology; (2) building a recurrent configuration of nonlinear autoregressive with exogenous inputs (R-NARX) to forecast the total inundated volume; and (3) adjusting the weights of the selected neuron in the constructed SOM based on the forecasted total inundated volume to obtain a real-time adapted regional inundation map. The proposed models are trained and tested based on a large number of inundation data sets collected in an inundation-prone region (270Â km2) in the Yilan County, Taiwan. The results show that (1) the SOM-R-NARX model can suitably forecast multi-step-ahead regional inundation maps; and (2) the SOM-R-NARX model consistently outperforms the comparative model in providing regional inundation maps with smaller forecast errors and higher correlation (RMSEÂ <Â 0.1Â m and R2Â >Â 0.9 in most cases). The proposed modelling approach offers an insightful and promising methodology for real-time forecasting 2-dimensional visible inundation maps during storm events.