Journal
NATURE COMMUNICATIONS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-15734-7
Keywords
-
Categories
Funding
- North Region Water Resources Office, Water Resources Agency (WRA), Ministry of Economic Affairs, Taiwan [MOEAWRA1030184]
- Ministry of Science and Technology, Taiwan [NSC 101-2313-B-032-002-MY3, 103-2313-B-002-016MY3]
Ask authors/readers for more resources
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. Tropical cyclones can cause severe flooding when making landfall, but these water flows can often only be forecasted a few hours before. Here, the authors present a new approach using self-organizing maps and flow characteristic curves to predict tropical cyclone related runoff up to two days in advance.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available