In this study,six intensity forecast guidance techniques from the East China Regional Meteorological Center are verified for the 2008 and 2009 typhoon seasons through an alternative forecast verification technique.Thi...In this study,six intensity forecast guidance techniques from the East China Regional Meteorological Center are verified for the 2008 and 2009 typhoon seasons through an alternative forecast verification technique.This technique is used to verify intensity forecasts if those forecasts call for a typhoon to dissipate or if the real typhoon dissipates.Using a contingency table,skill scores,chance,and probabilities are computed.It is shown that the skill of the six tropical cyclone intensity guidance techniques was highest for the 12-h forecasts,while the lowest skill of all the six models did not occur in 72-h forecasting.For both the 2008 and 2009 seasons,the average probabilities of the forecast intensity having a small error(6 m s-1) tended to decrease steadily.Some of the intensity forecasts had small skill scores,but the associated probabilities of the forecast intensity errors > 15 m s-1 were not the highest.展开更多
A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improv...A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.展开更多
基金supported by the National Basic Research Program of China (2009CB421505)the Shanghai Typhoon Foundation (2009ST09)+1 种基金the National Natural Science Foundation of China (40775060)the Program of China Mete-orological Administration (GYHY201006008 and GYHY200906002)
文摘In this study,six intensity forecast guidance techniques from the East China Regional Meteorological Center are verified for the 2008 and 2009 typhoon seasons through an alternative forecast verification technique.This technique is used to verify intensity forecasts if those forecasts call for a typhoon to dissipate or if the real typhoon dissipates.Using a contingency table,skill scores,chance,and probabilities are computed.It is shown that the skill of the six tropical cyclone intensity guidance techniques was highest for the 12-h forecasts,while the lowest skill of all the six models did not occur in 72-h forecasting.For both the 2008 and 2009 seasons,the average probabilities of the forecast intensity having a small error(6 m s-1) tended to decrease steadily.Some of the intensity forecasts had small skill scores,but the associated probabilities of the forecast intensity errors > 15 m s-1 were not the highest.
基金Projects 50774080 supported by the National Natural Science Foundation of China200348 by the Foundation for the National Excellent Doctoral Dis-sertation of China
文摘A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.
