Critical rainfall estimation for early warning of rainstorm-induced flash flood is an inverse rainstorm-runoff process based on warning discharge threshold for a warning station of interest in a watershed. The key asp...Critical rainfall estimation for early warning of rainstorm-induced flash flood is an inverse rainstorm-runoff process based on warning discharge threshold for a warning station of interest in a watershed. The key aspects of critical rainfall include rainfall amount and rainfall duration. Storm pattern affects highly the estimation of critical rainfall. Using hydrological modeling technique with detailed sub-basin delineation and manual for design rainstorm-runoff computation, this study first introduced basic concept and analysis methods on critical rainfall for flash flood early warning, then, investigated the responses of flash flood warning critical rainfall to storm pattern. Taking south branch of Censhui watershed in China as an example, critical rainfall in case of typical storm patterns for early warning of rainstorm-induced flash flood were estimated at 3 warning stations. This research illustrates that storm pattern plays important role in the estimation of critical rainfall and enough attention should also be paid to storm pattern when making a decision on whether a warning to be issued or not.展开更多
In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical ...In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.展开更多
Flash floods are one of the most devastating natural hazards in mountainous and hilly areas.In this study,a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the ...Flash floods are one of the most devastating natural hazards in mountainous and hilly areas.In this study,a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning.A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database.The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data.Subsequently,the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS(Hydrologic Engineering Center's Hydrologic Modeling System)model.To verify the proposed model,three small watersheds in China were selected as case studies.The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall.With the increase of measured rainfall information,the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency(NSE)exceeding 0.8 at t=4,t=5,and t=6,thereby improving the accuracy of flash flood warnings.Simultaneously,the proposed model extends the forecast lead time with high accuracy.For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed,the proposed model issues early warnings two hours and three hours before the end of the rainfall,respectively,with a warning accuracy of more than 0.90.The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.展开更多
文摘Critical rainfall estimation for early warning of rainstorm-induced flash flood is an inverse rainstorm-runoff process based on warning discharge threshold for a warning station of interest in a watershed. The key aspects of critical rainfall include rainfall amount and rainfall duration. Storm pattern affects highly the estimation of critical rainfall. Using hydrological modeling technique with detailed sub-basin delineation and manual for design rainstorm-runoff computation, this study first introduced basic concept and analysis methods on critical rainfall for flash flood early warning, then, investigated the responses of flash flood warning critical rainfall to storm pattern. Taking south branch of Censhui watershed in China as an example, critical rainfall in case of typical storm patterns for early warning of rainstorm-induced flash flood were estimated at 3 warning stations. This research illustrates that storm pattern plays important role in the estimation of critical rainfall and enough attention should also be paid to storm pattern when making a decision on whether a warning to be issued or not.
基金supported by the National Postdoctoral Researcher Program of China(No.GZC20231451)the National Natural Science Foundation of China(Nos.61890922,62203263)the Shandong Province Natural Science Foundation(Nos.ZR2020ZD40,ZR2022QF062).
文摘In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3004401)the National Natural Science Foundation of China(Grant No.52109040)。
文摘Flash floods are one of the most devastating natural hazards in mountainous and hilly areas.In this study,a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning.A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database.The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data.Subsequently,the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS(Hydrologic Engineering Center's Hydrologic Modeling System)model.To verify the proposed model,three small watersheds in China were selected as case studies.The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall.With the increase of measured rainfall information,the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency(NSE)exceeding 0.8 at t=4,t=5,and t=6,thereby improving the accuracy of flash flood warnings.Simultaneously,the proposed model extends the forecast lead time with high accuracy.For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed,the proposed model issues early warnings two hours and three hours before the end of the rainfall,respectively,with a warning accuracy of more than 0.90.The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.
