[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-d...[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.展开更多
A dual-resolution（DR） version of a regional ensemble Kalman filter（EnKF）-3D ensemble variational（3DEnVar） coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh f...A dual-resolution（DR） version of a regional ensemble Kalman filter（EnKF）-3D ensemble variational（3DEnVar） coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution（HR） deterministic background forecast with lower-resolution（LR） EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation（GSI） 3D variational（3DVar）analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.展开更多
Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest.Traditional forward research methods are unable to manipulate s...Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest.Traditional forward research methods are unable to manipulate sound waves accurately and effectively,are difficult to analyse,have time-consuming solution processes,and have large optimization search spaces.To address these issues,this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance.First,an autoencoder(AE)model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating,the material parameters are extracted into the latent feature space by the encoder,and the decoder model is saved.The internal relationship between the reflection coefficient and latent feature space is trained to establish a multilayer perceptron(MLP).Then,the reflection coefficients in the test set are input to the trained MLP and decoder models to automatically invert the material parameters.The accuracy of the inversion result is 95.08%.Finally,a predictive model is trained to rapidly predict the acoustic performance of the inverted material parameters.The speed of a single test target is 80 times faster than that of the finite element method(FEM).Furthermore,sound absorber material parameters with the best sound absorption performance and a three-band sound absorber are inverted,and their actual sound absorption performance is predicted by the proposed method.The proposed deep learning-based inversion research method provides a solution for low-frequency,wide-band,strong attenuation,and precisely controlled sound waves.It achieves an efficient inversion of material parameters and the rapid forecasting of acoustic performance.The training model can be used for a sound absorbing coating composed of irregular cavities in a viscoelastic material and predict its acoustic performance by only modifying the dataset.展开更多
文摘[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.
基金supported by the National Natural Science Foundation of China (Grant Nos.41730965,41775099 and 2017YFC1502104)PAPD (the Priority Academic Program Development of Jiangsu Higher Education Institutions)
文摘A dual-resolution（DR） version of a regional ensemble Kalman filter（EnKF）-3D ensemble variational（3DEnVar） coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution（HR） deterministic background forecast with lower-resolution（LR） EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation（GSI） 3D variational（3DVar）analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.
基金supported by the National Natural Science Foundation of China(Nos.51765008,11304050)the High-Level Innovative Talents Project of Guizhou Province(No.20164033)+1 种基金the Science and Technology Project of Guizhou Province(No.2020–1Z048)the Open Project of the Key Laboratory of Modern Manufacturing Technology of the Ministry of Education(No.XDKFJJ[2016]10).
文摘Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest.Traditional forward research methods are unable to manipulate sound waves accurately and effectively,are difficult to analyse,have time-consuming solution processes,and have large optimization search spaces.To address these issues,this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance.First,an autoencoder(AE)model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating,the material parameters are extracted into the latent feature space by the encoder,and the decoder model is saved.The internal relationship between the reflection coefficient and latent feature space is trained to establish a multilayer perceptron(MLP).Then,the reflection coefficients in the test set are input to the trained MLP and decoder models to automatically invert the material parameters.The accuracy of the inversion result is 95.08%.Finally,a predictive model is trained to rapidly predict the acoustic performance of the inverted material parameters.The speed of a single test target is 80 times faster than that of the finite element method(FEM).Furthermore,sound absorber material parameters with the best sound absorption performance and a three-band sound absorber are inverted,and their actual sound absorption performance is predicted by the proposed method.The proposed deep learning-based inversion research method provides a solution for low-frequency,wide-band,strong attenuation,and precisely controlled sound waves.It achieves an efficient inversion of material parameters and the rapid forecasting of acoustic performance.The training model can be used for a sound absorbing coating composed of irregular cavities in a viscoelastic material and predict its acoustic performance by only modifying the dataset.
