Abstract. This study presents a methodology for forecasting the extent of inundation and depth of distribution during typhoons in real-time. The proposed approach involves the construction of ARX and ARMAX models capable of predicting water-levels at the locations of on-site gauging stations and 10 representative points located at the outlets of the sub-areas obtained by terrain analysis using a geographic information system. The models are constructed based on historical typhoon data and the results of numerical simulations related to inundation. A database comprising layers of inundation maps related to water-levels in each sub-area based on the assumption of flat-water and the digital elevation model (DEM) of the area were assembled prior to the typhoon. Water-levels during the typhoon are forecast using the 15 constructed models, whereupon inundation sub-maps associated with the forecasted water-levels are extracted from the database. The resulting inundation map is comparable to that obtained using Synthetic Aperture Radar. Processing can be conducted in real-time and requires very little computational resources. This provides valuable lead time in which to conduct efforts aimed at damage mitigation during a typhoon. 20

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Accurate inundation level forecasting during typhoon invasion is crucial for organizing response actions such as the evacuation of people from areas that could potentially flood. This paper explores the ability of nonlinear autoregressive neural networks with exogenous inputs (NARX) to predict inundation levels induced by typhoons. Two types of NARX architecture were employed: series-parallel (NARX-S) and parallel (NARX-P). Based on cross-correlation analysis of rainfall and water-level data from historical typhoon records, 10 NARX models (five of each architecture type) were constructed. The forecasting ability of each model was assessed by considering coefficient of efficiency (CE), relative time shift error (RTS), and peak water-level error (PE). The results revealed that high CE performance could be achieved by employing more model input variables. Comparisons of the two types of model demonstrated that the NARX-S models outperformed the NARX-P models in terms of CE and RTS, whereas both performed exceptionally in terms of PE and without significant difference. The NARX-S and NARX-P models with the highest overall performance were identified and their predictions were compared with those of traditional ARX-based models. The NARX-S model outperformed the ARX-based models in all three indexes, whereas the NARX-P model exhibited comparable CE performance and superior RTS and PE performance.

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