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Open AccessArticle

A Data-Driven Probabilistic Rainfall-Inundation Model for Flash-Flood Warnings

1
Center for Weather Climate and Disaster Research, National Taiwan University, Taipei 10617, Taiwan
2
Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan
3
Research Center of Climate Change and Sustainable Development, National Taiwan University, Taipei 10617, Taiwan
*
Author to whom correspondence should be addressed.
Water 2019, 11(12), 2534; https://doi.org/10.3390/w11122534
Received: 20 October 2019 / Revised: 17 November 2019 / Accepted: 27 November 2019 / Published: 30 November 2019
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
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. View Full-Text
Keywords: modified support vector machine; data-driven probabilistic rainfall-inundation model; flash-flood; early warning; rainfall pattern analysis; rainfall threshold modified support vector machine; data-driven probabilistic rainfall-inundation model; flash-flood; early warning; rainfall pattern analysis; rainfall threshold
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Pan, T.-Y.; Lin, H.-T.; Liao, H.-Y. A Data-Driven Probabilistic Rainfall-Inundation Model for Flash-Flood Warnings. Water 2019, 11, 2534.

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