Regional flood inundation nowcast using hybrid SOM and dynamic neural networks

This 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 (270 km(2)) 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 (RMSE < 0.1 m and R-2 > 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. (C) 2014 Elsevier B.V. All rights reserved.