Abstract
Real-time flood forecasting of small- and medium-sized rivers in areas with scarce hydrological data is an urgent problem that needs to be solved. Traditional hydrological model parameters cannot be fully trained owing to a lack of data; thus, results obtained by such models are not satisfactory. We need a new way to solve the forecasting problem for small- and medium-sized rivers. We found that the time series of some feature variables have evident change trajectories in spatial dimension, and the change of some feature variables in the spatial dimension has a decisive influence on flooding processes, such as the spatial distribution of rainfall. To reflect the change of feature variables in spatial dimension with to solve the problem of the lack of hydrological data, we constructed a rainfall-flow pattern composed of a spatial-temporal dynamic time warping algorithm and multi-feature algorithm to measure the similarity of hydrological time series. In the experimental watersheds, we used rainfall-flow patterns to forecast the short-term flood streamflow, and satisfactory results were obtained. This suggests that the algorithm is suitable for hydrological studies and improves the accuracy of real-time flood forecasting for longer forecast periods.
Highlights
In China, there are nearly 9,000 small- and medium-sized rivers, covering an area of 200 to 3000 km2
2) We propose a rainfall-flow pattern matching method that overcomes the paucity of hydrological data in small and medium watersheds
We developed a different model: using a rainfall-flow pattern based on historical rainfall and flood flow data for real-time predictions of short-term flood stream-flow
Summary
In China, there are nearly 9,000 small- and medium-sized rivers, covering an area of 200 to 3000 km. Because of the optimization and popularization of deep learning algorithms, the data-driven model is widely used in stream-flow forecasting of large rivers. Flood forecasting in small and medium-sized watersheds is influenced by many factors, such as soil water content and rainfall center location. The initial soil water content and the movement trajectory of rainfall play an important role; these factors are not easy to measure directly, resulting in a small amount of data obtained To address these limitations, this study proposes a short-term flood forecasting model using feature factor decomposition in conjunction with weather-time rainfall-flow pattern matching. 2) We propose a rainfall-flow pattern matching method that overcomes the paucity of hydrological data in small and medium watersheds.
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