The application of conventional flood operation regulation is restricted due to insufficient description of flood control rules for the Pubugou Reservoir in southern China. Based on the requirements of different flood...The application of conventional flood operation regulation is restricted due to insufficient description of flood control rules for the Pubugou Reservoir in southern China. Based on the requirements of different flood control objects, this paper proposes to optimize flood control rules with punishment mechanism by defining different parameters of flood control rules in response to flood inflow forecast and reservoir water level. A genetic algorithm is adopted for solving parameter optimization problem. The failure risk and overflow volume of the downstream insufficient flood control capacity are assessed through the reservoir operation policies. The results show that an optimised regulation can provide better performance than the current flood control rules.展开更多
To reduce the computational cost,we propose a regularizing modified LevenbergMarquardt scheme via multiscale Galerkin method for solving nonlinear ill-posed problems.Convergence results for the regularizing modified L...To reduce the computational cost,we propose a regularizing modified LevenbergMarquardt scheme via multiscale Galerkin method for solving nonlinear ill-posed problems.Convergence results for the regularizing modified Levenberg-Marquardt scheme for the solution of nonlinear ill-posed problems have been proved.Based on these results,we propose a modified heuristic parameter choice rule to terminate the regularizing modified Levenberg-Marquardt scheme.By imposing certain conditions on the noise,we derive optimal convergence rates on the approximate solution under special source conditions.Numerical results are presented to illustrate the performance of the regularizing modified Levenberg-Marquardt scheme under the modified heuristic parameter choice.展开更多
基金funded by the National Natural Science Foundations of China (Nos. 51179130 and 51190094)
文摘The application of conventional flood operation regulation is restricted due to insufficient description of flood control rules for the Pubugou Reservoir in southern China. Based on the requirements of different flood control objects, this paper proposes to optimize flood control rules with punishment mechanism by defining different parameters of flood control rules in response to flood inflow forecast and reservoir water level. A genetic algorithm is adopted for solving parameter optimization problem. The failure risk and overflow volume of the downstream insufficient flood control capacity are assessed through the reservoir operation policies. The results show that an optimised regulation can provide better performance than the current flood control rules.
基金Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University(2020B1212060032)Key Laboratory of Jiangxi Province for Numerical Simulation and Emulation Techniques(400440)+1 种基金the Foundation of Education Committee of Jiangxi,China(GJJ201436)National Natural Science Foundation of China under grants 11571386 and 11761010.
文摘To reduce the computational cost,we propose a regularizing modified LevenbergMarquardt scheme via multiscale Galerkin method for solving nonlinear ill-posed problems.Convergence results for the regularizing modified Levenberg-Marquardt scheme for the solution of nonlinear ill-posed problems have been proved.Based on these results,we propose a modified heuristic parameter choice rule to terminate the regularizing modified Levenberg-Marquardt scheme.By imposing certain conditions on the noise,we derive optimal convergence rates on the approximate solution under special source conditions.Numerical results are presented to illustrate the performance of the regularizing modified Levenberg-Marquardt scheme under the modified heuristic parameter choice.
