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Abstract. This study attempts to achieve real-time rainfallinundation forecasting in lowland regions, based on a synthetic potential inundation database. With the principal component analysis and a feed-forward neural network, a rainfall-inundation hybrid neural network (RiHNN) is proposed to forecast 1-h-ahead inundation depth as hydrographs at specific representative locations using spatial rainfall intensities and accumulations. A systematic procedure is presented to construct the RiHNN, which combines the merits of detailed hydraulic modeling in flood-prone lowlands via a two-dimensional overland-flow model and time-saving calculation in a real-time rainfall-inundation forecasting via ANN model. Analytical results from the RiHNNs with various principal components indicate that the RiHNNs with fewer weights can have about the same performance as a feed-forward neural network. The RiHNNs evaluated through four types of real/synthetic rainfall events also show to fit inundation-depth hydrographs well with high rainfall. Moreover, the results of real-time rainfall-inundation forecasting help the emergency manager set operational responses, which are beneficial for flood warning preparations.

Owing to their short duration and high intensity, flash floods are among the most devastating natural disasters in metropolises. The existing warning tools-flood potential maps and two-dimensional numerical models-are disadvantaged by time-consuming computation and complex model calibration. This study develops a data-driven, probabilistic rainfall-inundation model for flash-flood warnings. Applying a modified support vector machine (SVM) to limited flood information, the model provides probabilistic outputs, which are superior to the Boolean functions of the traditional rainfall-flood threshold method. The probabilistic SVM-based model is based on a data preprocessing framework that identifies the expected durations of hazardous rainfalls via rainfall pattern analysis, ensuring satisfactory training data, and optimal rainfall thresholds for validating the input/output data. The proposed model was implemented in 12 flash-flooded districts of the Xindian River. It was found that (1) hydrological rainfall pattern analysis improves the hazardous event identification (used for configuring the input layer of the SVM); (2) brief hazardous events are more critical than longer-lasting events; and (3) the SVM model exports the probability of flash flooding 1 to 3 h in advance. Such a combined model is quick and suitable for fluvial floods, but inapplicable to pluvial flash floods. A similar combined hydrological model, which derives the rainfall thresholds in pluvial flash floods, was considered by Forestieri et al. (2016) [5]. In natural hazards induced by short-term heavy rainfall such as flash floods or landslides, a timely warning is more important than evaluating the disaster impacts on people. Therefore, the empirical rainfall thresholds of flash floods and rainfall-induced landslides have been increasingly sought in recent years [5][6][7][8][9][10][11][12][13][14][15].The local governments in Taiwan also consider the rainfall thresholds in flash floods and landslides [16][17][18]. In the official flash-flood warning system of Taiwan, the rainfall thresholds in all counties and districts are decided from five cumulative rainfalls of durations 1, 3, 6, 12, and 24 h. At least one rain gauge is assigned as the reference rain gauge for each county or district [19]. In 2017, the Water Resources Agency (WRA) used 754 sets of rainfall thresholds based on 500 rain gauges and warned 368 counties and districts of pending flash floods in Taiwan. However, the rainfall thresholds are based on historical flood records, which lack the parameters (start times, areas, and durations of the floods) required in complex flood warning models. Most of the historical flood records in Taiwan are reports of people's phone calls, which are checked by officers several hours later. Therefore, when determining rainfall thresholds, the historical flood records of the WRA are useful only for predicting whether a rainfall event will cause a disaster. Although potential inundation maps improve the information input [18,20,21], the models based on th...

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Typhoon Morakot 2009, with significant southwest monsoon flow, produced a record-breaking rainfall of 2361 mm in 48 hours. This study hopes to improve a statistical typhoon rainfall forecasting method used over the mountain region of Taiwan via an artificial neural network based southwest monsoon enhancement (ANNSME) model. Rainfall data collected at two mountain weather stations, ALiShan and YuShan, are analyzed to establish the relation to the southwest monsoon moisture flux which is calculated at a designated sea area southwest of Taiwan. The results show that the moisture flux, with southwest monsoon flow, transported water vapor during the landfall periods of Typhoons Mindulle, Bilis, Fungwong, Kalmaegi, Haitaing and Morakot. Based on the moisture flux, a linear regression is used to identify an effective value of moisture flux as the threshold flux which can enhance mountain rainfall in southwestern Taiwan. In particular, a feedforward neural network (FNN) is applied to estimate the residuals from the linear model to the differences between simulated rainfalls by a typhoon rainfall climatology model (TRCM) and observations. Consequently, the ANNSME model integrates the effective moisture flux, linear rainfall model and the FNN for residuals. Even with very limited training cases, our results indicate that the ANNSME model is robust and suitable for improvement of TRCM rainfall prediction. The improved prediction of the total rainfall and of the multiple rainfall peaks is important for emergency operation.