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China is one of the countries in the world that seriously affected by flash floods disasters. The flash flood caused by extreme rainfall occurred at mountainous small-sized watersheds in China often leads to serious economic damages and obstructs the social development. Setting up an efficient forecasting system for flash flood has been widely accepted as one of the key non-structural measures to improve the control and prevention capability of China. However, due to the data limitation, establishing forecast models in those flash flood areas is challenged by the lack of parameter references. This paper proposed a new machine learning approach based on the Random Forest (RF) algorithm for model parameter regionalization. Integrated with distributed deterministic hydrological models of 20 small-sized watersheds in Henan province, the RF algorithm has been applied for defining the watersheds’ similarity and further transferring the parameters from sample watersheds to the objective watershed. Validated through leave-one-out approach, the RF model is able to effectively improve the simulation accuracy of flash floods in Henan province. The presented approach showed high-levelled applicability to be extended in other flash flood areas in China for providing effective reference for parameter regionalization.

A New Machine Learning Approach for parameter regionalization of Flash Flood Modelling in Henan Province, China