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Hydrological time series refers to the observation time point and the observed time value. The simulation and prediction of hydrological time series will greatly improve the predictability of hydrological time series, which is of great significance for hydrological forecasting. TensorFlow, the second generation of artificial intelligence learning system in Google, has been favored by a large number of researchers by virtue of its high flexibility, portability, multi-language support, and performance optimization. However, the application of deep learning in hydrology is less. Based on the TensorFlow framework, the AR model and the LSTM model are constructed in Python language. The hydrological time series is used as the input object, and the model is deeply studied and trained to simulate and predict the hydrological time series. The effect of the model was tested by fitting degrees and other indexes. The fitting degree of the AR model is 0.9551, and the fitting degree of the LSTM model is 0.8012, which shows the feasibility of the model for predicting the hydrological time series, and puts forward the solution for the limitation of the existing analysis results.
The flash flood early warning method based on dynamic critical precipitation is proposed, which takes into account the percentage saturation of soil moisture content in double-excess model. A series of historical precipitation data of various gauge stations in the upper catchment of the study area at the early warning cross-section are set as the input parameters, thereby the runoff generation and concentration in the catchment are obtained in the double-excess model, and the percent saturation of soil moisture content is calculated. Based on the warning discharge in combination with the percentage saturation of soil moisture content, the discriminant relations of the critical precipitation for the time intervals, including 0.5 h, 1 h, 1.5 h, 2 h, 2.5 h, and 3 h, are computed respectively using the inversion method. Using the precipitation data from ground rain gauge stations for year x and flood hydrograph data of x typical flood events for the Dayuhe River catchment, the SCE-UA algorithm is adopted to calibrate the parameters of the double-excess model, and the discriminant functions of dynamic critical precipitation for flash flood early warning with 6 time scales are validated using x representative historical flood hydrographs. The qualification ratio for flash flood early warning exceeds x, which demonstrates the feasibility and applicability of the proposed method.
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