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

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 t

SUBMITTER: Chang LC 

PROVIDER: S-EPMC7181664 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature