Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance

Li-Chiu Chang (), Fi-John Chang (), Shun-Nien Yang, Fong-He Tsai, Ting-Hua Chang and Edwin E. Herricks
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Li-Chiu Chang: Tamkang University
Fi-John Chang: National Taiwan University
Shun-Nien Yang: Tamkang University
Fong-He Tsai: National Taiwan University
Ting-Hua Chang: Ministry of Economic Affairs
Edwin E. Herricks: University of Illinois at Urbana-Champaign

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: Abstract Typhoons are among the greatest natural hazards along East Asian coasts. Typhoon-related precipitation can produce flooding that is often only predictable a few hours in advance. Here, we present a machine-learning method comparing projected typhoon tracks with past trajectories, then using the information to predict flood hydrographs for a watershed on Taiwan. The hydrographs provide early warning of possible flooding prior to typhoon landfall, and then real-time updates of expected flooding along the typhoon’s path. The method associates different types of typhoon tracks with landscape topography and runoff data to estimate the water inflow into a reservoir, allowing prediction of flood hydrographs up to two days in advance with continual updates. Modelling involves identifying typhoon track vectors, clustering vectors using a self-organizing map, extracting flow characteristic curves, and predicting flood hydrographs. This machine learning approach can significantly improve existing flood warning systems and provide early warnings to reservoir management.

Date: 2020
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DOI: 10.1038/s41467-020-15734-7

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