To reduce typhoon-caused damages, numerical and empirical methods are often used to forecast typhoon storm surge. However, typhoon surge is a complex nonlinear process that is difficult to forecast accurately. We applied a principal component back-propagation neural network (PCBPNN) to predict the deviation in typhoon storm surge, in which data of the typhoon, upstream flood, and historical case studies were involved. With principal component analysis, 15 input factors were reduced to five principal components, and the application of the model was improved. Observation data from Huangpu Park in Shanghai, China were used to test the feasibility of the model. The results indicate that the model is capable of predicting a 12-hour warning before a typhoon surge.
Uncertainty in 3D geological structure models has become a bottleneck that restricts the development and application of 3D geological modeling.In order to solve this problem during periods of accuracy assessment,error detection and dynamic correction in 3D geological structure models,we have reviewed the current situation and development trends in 3D geological modeling.The main context of uncertainty in 3D geological structure models is discussed.Major research issues and a general framework system of uncertainty in 3D geological structure models are proposed.We have described in detail the integration of development practices of 3D geological modeling systems,as well as the implementation process for uncertainty evaluation in 3D geological structure models.This study has laid the basis to build theoretical and methodological systems for accuracy assessment and error correction in 3D geological models and can assist in improving 3D modeling techniques under complex geological conditions.
