Improved Dynamic System Response Curve Method for Real‐Time Flood Forecast Updating

Abstract

The dynamic system response curve (DSRC) method has been shown to effectively use error feedback correction to obtain updated areal estimates of mean rainfall and thereby improve the accuracy of real‐time flood forecasts. In this study, we address two main shortcomings of the existing method. First, ridge estimation is used to deal with ill‐conditioning of the normal equation coefficient matrix when the method is applied to small basins, or when the length of updating rainfall series is short. Second, the effects of spatial heterogeneity of rainfall on rainfall error estimates are accounted for using a simple index. The improved performance of the method is demonstrated using both synthetic and real data studies. For smaller basins with relatively homogeneous spatial distributions of rainfall, the use of ridge regression provides more accurate and robust results. For larger‐scale basins with significant spatial heterogeneity of rainfall, spatial rainfall error updating provides significant improvements. Overall, combining the two strategies results in the best performance for all cases, with the effects of ridge estimation and spatially distributed updating complementing each other.

Improvement in Ridge Coefficient Optimization Criterion for Ridge Estimation-Based Dynamic System Response Curve Method in Flood Forecasting

“…Correction results from the DSRC method are sometimes unstable. Relevant studies [19,20] have pointed out that the DSRC method is prone to be ill-conditioned, which generates unstable results when this method is applied to small basins, or when the length of flow information is short. Therefore, Si et al [19] combined the DSRC method with the ridge estimation method and proposed the DSRC-R method, which is more robust than the DSRC method.…”

“…Then, it was used to correct several hydrological elements comprehensively [18]. However, some studies [17][18][19][20] found that the correction results are not always stable, reflected in the excessive correction of hydrological element series and none-smooth simulated flow hydrographs. To solve the above problems, the DSRC-R method was developed by Si et al [19] from the point of the regularization known as ridge estimation, and this method has improved the stability of correction results to some degree.…”

“…Nevertheless, the selection criterion of the ridge coefficient still needs further study and improvement. Previous studies [17][18][19] often chose the ridge coefficient based on the L-curve criterion [21]. However, it has been found in practice that two aspects still need attention.…”

A Safety Collision Avoidance Algorithm Based on Comprehensive Characteristics

“…at information can be obtained from the vehicle's own sensors and Electronic Control Unit (ECU), and the vehicle's position coordinates and heading are obtained through the GPS navigation and positioning system [28]. In the Controller Area Network (CAN) bus of the automobile, the vehicle parameters can be obtained to determine whether the vehicle is in an emergency state such as the tire pressure of the vehicle and the shortage of oil [29], and the prompt is given in the terminal system. en, through real-time acquisition of the state information of the car and the adjacent car, including the latitude, longitude, vehicle speed, and heading angle information of the vehicle, these data are entered into a safety collision avoidance algorithm for processing and alert when there is a danger.…”

Data‐driven model for river flood forecasting based on a Bayesian network approach

“…Yet, the dynamic management of flood‐type events requires tools capable of reacting quickly to the input of new information in order to contextualize decision‐making with regard to the state of the descriptive variables of the analysed situation. In particular, in small catchment characterized by fast response time, and where flash flood are often destructive, there is a need for real‐time flood forecasts (Ruin, Creutin, Anquetin, & Lutoff, 2008, Roux, 2011; Si, 2019).…”