Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-value...Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.展开更多
With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration predict...With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.展开更多
The real-time prediction of head warping and lower buckling during the production process of rough-rolled slabs has long been a persistent technical problem at the production site.An online real-time prediction system...The real-time prediction of head warping and lower buckling during the production process of rough-rolled slabs has long been a persistent technical problem at the production site.An online real-time prediction system was proposed for head warping and lower buckling of rough-rolled slab based on mechanism-data dual drive.The modified Johnson–Cook constitutive model was derived and established to provide parameter support for the finite element simulation.Visual detection technology was employed to provide data.Industrial applications showed that the prediction accuracy of the prediction system described is as follows:prediction error≤±3 mm,type prediction rate≥98%.Moreover,the head warping and lower buckling of slab in the production site have been significantly improved,and the shape quality of slab has been increased by 3 times compared with that before adjustment,which meet site production requirements.展开更多
Recently, a coupled data assimilation system based on the community earth system model(CESM) and ensemble adjustment Kalman filter(EAKF) has been established to assimilate various ocean observations including gridded ...Recently, a coupled data assimilation system based on the community earth system model(CESM) and ensemble adjustment Kalman filter(EAKF) has been established to assimilate various ocean observations including gridded sea surface temperature and in situ temperature and salinity profiles for the initialization of seasonal prediction. The main goal of the present study is to assess the El Nino-Southern Oscillation(ENSO) prediction capability of the newly developed system(CESM-E). We compare it with a benchmark prediction system based on the same model but employing a nudging scheme(CESM-N), which nudged the wind fields and ocean temperature. Results have found that although the initial subsurface temperature are comparable in the two systems, CESM-E outperforms CESM-N in a few aspects. For example, CESM-E exhibits clearly lower root mean square errors in the first few leading months and higher anomaly correlation coefficients in the Nino4 region. In addition, case studies reveal that CESM-E is clearly better in predicting the 2006/2007 El Nino and 2010/2011 La Nina events. Reasons behind the improvement of CESME are studied, which can provide useful insights into the design of a data assimilation system and the further improvement of current ENSO prediction system.展开更多
In the context of global change,understanding changes in water resources requires close monitoring of groundwater levels.A mismatch between water supply and demand could lead to severe consequences such as land subsid...In the context of global change,understanding changes in water resources requires close monitoring of groundwater levels.A mismatch between water supply and demand could lead to severe consequences such as land subsidence.To ensure a sustainable water supply and to minimize the environmental effects of land subsidence,groundwater must be effectively monitored and managed.Despite significant global progress in groundwater management,the swift advancements in technology and artificial intelligence(AI)have spurred extensive studies aimed at enhancing the accuracy of groundwater predictions.This study proposes an AI-based method that combines deep learning with a cloud-supported data processing workflow.The method utilizes river level data from the Zhuoshui River alluvial fan area in Taiwan,China to forecast groundwater level fluctuations.A hybrid imputation scheme is applied to reduce data errors and improve input continuity,including Z-score anomaly detection,sliding window segmentation,and STL-SARIMA-based imputation.The prediction model employs the BiLSTM model combined with the Bayesian optimization algorithm,achieving an R2 of 0.9932 and consistently lower MSE values than those of the LSTM and RNN models across all experiments.Specifically,BiLSTM reduces MSE by 62.9%compared to LSTM and 72.6%compared to RNN,while also achieving the lowest MAE and MAPE scores,demonstrating its superior accuracy and robustness in groundwater level forecasting.This predictive advantage stems from the integration of a hybrid statistical imputation process with a BiLSTM model optimized through Bayesian search.These components collectively enable a reliable and integrated forecasting system that effectively models groundwater level variations,thereby providing a practical solution for groundwater monitoring and sustainable water resource management.展开更多
This paper explores the possibility of using machine learning algorithms to predict type 2 diabetes.We selected two commonly used classification models:random forest and logistic regression,modeled patients’clinical ...This paper explores the possibility of using machine learning algorithms to predict type 2 diabetes.We selected two commonly used classification models:random forest and logistic regression,modeled patients’clinical and lifestyle data,and compared their prediction performance.We found that the random forest model achieved the highest accuracy,demonstrated excellent classification results on the test set,and better distinguished between diabetic and non-diabetic patients by the confusion matrix and other evaluation metrics.The support vector machine and logistic regression perform slightly less well but achieve a high level of accuracy.The experimental results validate the effectiveness of the three machine learning algorithms,especially random forest,in the diabetes prediction task and provide useful practical experience for the intelligent prevention and control of chronic diseases.This study promotes the innovation of the diabetes prediction and management model,which is expected to alleviate the pressure on medical resources,reduce the burden of social health care,and improve the prognosis and quality of life of patients.In the future,we can consider expanding the data scale,exploring other machine learning algorithms,and integrating multimodal data to further realize the potential of artificial intelligence(AI)in the field of diabetes.展开更多
An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of...An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.展开更多
This paper investigates the possible sources of errors associated with tropical cyclone （TC） tracks forecasted using the Global/Regional Assimilation and Prediction System （GRAPES）. The GRAPES forecasts were made ...This paper investigates the possible sources of errors associated with tropical cyclone （TC） tracks forecasted using the Global/Regional Assimilation and Prediction System （GRAPES）. The GRAPES forecasts were made for 16 landfaIling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions （＂initials＂, hereafter）, which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.展开更多
A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and India...A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and Indian Ocean Dipole(IOD)predictions.Compared with the origin nudging scheme of NMEFC,the new scheme adds a nudge assimilation for wind components,and increases the nudging weight at the subsurface.Increasing the nudging weight at the subsurface directly improved the simulation performance of the ocean component,while assimilating low-level wind components not only affected the atmospheric component but also benefited the oceanic simulation.Hindcast experiments showed that the new scheme remarkably improved both ENSO and IOD prediction skills.The skillful prediction lead time of ENSO was up to 11 months,1 month longer than a hindcast using the original nudging scheme.Skillful prediction of IOD could be made 4–5 months ahead by the new scheme,with a 0.2 higher correlation at a 3-month lead time.These prediction skills approach the level of some of the best state-of-the-art coupled general circulation models.Improved ENSO and IOD predictions occurred across all seasons,but mainly for target months in the boreal spring for the ENSO and the boreal spring and summer for the IOD.展开更多
The‘Two Oceans and One Sea’area(West Pacific,Indian Ocean,and South China Sea;15°S–60°N,39°–178°E)is a core strategic area for the‘21st Century Maritime Silk Road’project,as well as national ...The‘Two Oceans and One Sea’area(West Pacific,Indian Ocean,and South China Sea;15°S–60°N,39°–178°E)is a core strategic area for the‘21st Century Maritime Silk Road’project,as well as national defense.With the increasing demand for disaster prevention and mitigation,the importance of 10–30-day extended range prediction,between the conventional short-term(around seven days)and the climate scale(longer than one month),is apparent.However,marine extended range prediction is still a‘blank point’in China,making the early warning of marine disasters almost impossible.Here,the authors introduce a recently launched Chinese national project on a numerical forecasting system for extended range prediction in the‘Two Oceans and One Sea’area based on a regional ultra-high resolution multi-layer coupled model,including the scientific aims,technical scheme,innovation,and expected achievements.The completion of this prediction system is of considerable significance for the economic development and national security of China.展开更多
How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecast...How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts.In this study,a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System(GRAPES)Convection-Allowing Ensemble Prediction System(CAEPS).The nonlinear forcing singular vector(NFSV)approach,that is,conditional nonlinear optimal perturbation-forcing(CNOP-F),is applied in this study,to construct a nonlinear model perturbation method for GRAPES-CAEPS.Three experiments are performed:One of them is the CTL experiment,without adding any model perturbation;the other two are NFSV-perturbed experiments,which are perturbed by NFSV with two different groups of constraint radii to test the sensitivity of the perturbation magnitude constraint.Verification results show that the NFSV-perturbed experiments achieve an overall improvement and produce more skillful forecasts compared to the CTL experiment,which indicates that the nonlinear NFSV-perturbed method can be used as an effective model perturbation method for convection-scale ensemble forecasts.Additionally,the NFSV-L experiment with large perturbation constraints generally performs better than the NFSV-S experiment with small perturbation constraints in the verification for upper-air and surface weather variables.But for precipitation verification,the NFSV-S experiment performs better in forecasts for light precipitation,and the NFSV-L experiment performs better in forecasts for heavier precipitation,indicating that for different precipitation events,the perturbation magnitude constraint must be carefully selected.All the findings above lay a foundation for the design of nonlinear model perturbation methods for future CAEPSs.展开更多
The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Ph...The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Physics Dynamical Seasonal Prediction System (IAP DCP),along with the National Centers for Environmental Prediction (NCEP) reanalysis data from the period of 1980-2000.The large-scale characteristics of the SCSSM monthly and seasonal mean low-level circulation have been well reproduced by IAP DCP,especially for the zonal wind at 850 hPa;furthermore,the hindcast variability also agrees quite well with observations.By introducing the South China Sea summer monsoon index,the potential predictability of IAP DCP for the intensity of the SCSSM has been evaluated.IAP DCP showed skill in predicting the interannual variation of SCSSM intensity.The result is highly encouraging;the correlation between the hindcasted and observed SCSSM Index was 0.58,which passes the 95% significance test.The result for the seasonal mean June-July-August SCSSM Index was better than that for the monthly mean,suggesting that seasonal forecasts are more reliable than monthly forecasts.展开更多
Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In...Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.展开更多
The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the predict...The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the prediction skill of the system at a 10-day lead time for monthly TC activity is given based on 35-year(1981–2015)hindcasts with 24 ensemble members.The results show that FGOALS-f2 can capture the climatology of TC track densities in each month,but there is a delay in the monthly southward movement in the area of high track densities of TCs.The temporal correlation coefficient of TC frequency fluctuates across the different months,among which the highest appears in October(0.59)and the lowest in August(0.30).The rank correlation coefficients of TC track densities are relatively higher(R>0.6)in July,September,and November,while those in August and October are relatively lower(R within 0.2 to 0.6).For real-time prediction of TCs in 2020(July to November),FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement.Besides,the system successfully forecasts the correct sign of monthly anomalies of TC frequency and accumulated cyclone energy for 2020(July to November)in the SCS.展开更多
Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the model...Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.展开更多
Based on an Ensemble Prediction System with the BGM method on the regional numerical prediction model AREM, Static State Perturbation （SSP, including Initial Random Perturbation and Perturbation Restriction） and Dyn...Based on an Ensemble Prediction System with the BGM method on the regional numerical prediction model AREM, Static State Perturbation （SSP, including Initial Random Perturbation and Perturbation Restriction） and Dynamic State Perturbation （DSP） are designed. The impacts of both perturbations on precipitation prediction are studied by analyzing a slrong precipitation process oc- curring during July 20-21, 2008. The results show that both SSP and DSP play a positive role in prediction of mesoscale precipita- tion, such as lowering the （missing） rate of precipitation prediction. SSP is mainly helpful for the 24-hour prediction, while DSP can improve both 24-hour and 48-hour prediction. DSP is better than the two SSPs in the hit rate of regional precipitation prediction. However, the former also has a little higher false alarm rate than the latter. DSP enlarges in some extent the dispersion of EPS, which is good for EPS.展开更多
Objective: Analyzing 6 biomarkers, such as procalcitonin (PCT), C-reactive protein (CRP), fibrinogen (Fib), lactate concentration (Lac), D-dimer (D-d), neutrophil ratio (NEUT%) to figure out several sensitive indicato...Objective: Analyzing 6 biomarkers, such as procalcitonin (PCT), C-reactive protein (CRP), fibrinogen (Fib), lactate concentration (Lac), D-dimer (D-d), neutrophil ratio (NEUT%) to figure out several sensitive indicators and establish a new prediction system of sepsis, which could achieve a higher sensitivity and specificity to predict sepsis. Methods: We collected 113 SIRS patients in ICU. According to their prognosis, all the patients were divided into two groups named sepsis and non-sepsis group according to the diagnostic criteria of sepsis. We recorded the general information and detected the plasma levels of the 6 biomarkers. Results: The plasma levels of NEUT% and Fib between the two groups had no significant difference. PCT had the highest prediction accuracy of sepsis compared with other biomarkers. A predictive model was established, in which Lac, PCT, CRP were enrolled. The final prediction model was: logit(P) = 0.314 + 0.105 × Lac(mmol/l) + 0.099 × PCT(ng/mL) + 0.012 × CRP(mg/L). The area under the curve of the prediction model was 0.893, which was higher than every single biomarker involved in this study. Conclusion: The three serum biomarkers of Lac, PCT, CRP are used to establish a prediction model of sepsis: logit(P) = 0.314 + 0.105 × Lac(mmol/l) + 0.099 × PCT(ng/mL) + 0.012 × CRP(mg/L), which can better predict the occurrence of sepsis compared with other biomarkers.展开更多
A soft sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificia...A soft sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificial neural network was used in predicting BTP, modification on backpropagation algorithm was made in order to improve the convergence and self organize the hidden layer neurons. The adaptive prediction system developed on these techniques shows its characters such as fast, accuracy, less dependence on production data. The prediction of BTP can be used as operation guidance or control parameter.[展开更多
In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandem...In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.展开更多
This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). In Part I, it is shown that the model...This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II.Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60?S and 60?N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.展开更多
基金supported by the National Key Project of the Ministry of Science and Technology of China(No.2022YFC3701200)the National Natural Science Foundation of China(No.42090030).
文摘Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human interference.The evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,China.The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,respectively.Compared with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical models.The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.
基金supported by General Scientific Research Funding of the Science and Technology Development Fund(FDCT)in Macao(No.0150/2022/A)the Faculty Research Grants of Macao University of Science and Technology(No.FRG-22-074-FIE).
文摘With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.
基金supported by Regional Joint Funds of the National Natural Science Foundation of China(U20A20289)Innovative Talents International Cooperative Training Project of China Scholarship Council(CXXM20240010)+2 种基金The General Program of National Natural Science Foundation of China(52075471)Innovation Capacity Enhancement Program of Hebei Province(24461901D)National Natural Science Foundation of China(Grant No.52475409).
文摘The real-time prediction of head warping and lower buckling during the production process of rough-rolled slabs has long been a persistent technical problem at the production site.An online real-time prediction system was proposed for head warping and lower buckling of rough-rolled slab based on mechanism-data dual drive.The modified Johnson–Cook constitutive model was derived and established to provide parameter support for the finite element simulation.Visual detection technology was employed to provide data.Industrial applications showed that the prediction accuracy of the prediction system described is as follows:prediction error≤±3 mm,type prediction rate≥98%.Moreover,the head warping and lower buckling of slab in the production site have been significantly improved,and the shape quality of slab has been increased by 3 times compared with that before adjustment,which meet site production requirements.
基金The National Natural Science Foundation of China under contract Nos 42450178 and 42176003。
文摘Recently, a coupled data assimilation system based on the community earth system model(CESM) and ensemble adjustment Kalman filter(EAKF) has been established to assimilate various ocean observations including gridded sea surface temperature and in situ temperature and salinity profiles for the initialization of seasonal prediction. The main goal of the present study is to assess the El Nino-Southern Oscillation(ENSO) prediction capability of the newly developed system(CESM-E). We compare it with a benchmark prediction system based on the same model but employing a nudging scheme(CESM-N), which nudged the wind fields and ocean temperature. Results have found that although the initial subsurface temperature are comparable in the two systems, CESM-E outperforms CESM-N in a few aspects. For example, CESM-E exhibits clearly lower root mean square errors in the first few leading months and higher anomaly correlation coefficients in the Nino4 region. In addition, case studies reveal that CESM-E is clearly better in predicting the 2006/2007 El Nino and 2010/2011 La Nina events. Reasons behind the improvement of CESME are studied, which can provide useful insights into the design of a data assimilation system and the further improvement of current ENSO prediction system.
文摘In the context of global change,understanding changes in water resources requires close monitoring of groundwater levels.A mismatch between water supply and demand could lead to severe consequences such as land subsidence.To ensure a sustainable water supply and to minimize the environmental effects of land subsidence,groundwater must be effectively monitored and managed.Despite significant global progress in groundwater management,the swift advancements in technology and artificial intelligence(AI)have spurred extensive studies aimed at enhancing the accuracy of groundwater predictions.This study proposes an AI-based method that combines deep learning with a cloud-supported data processing workflow.The method utilizes river level data from the Zhuoshui River alluvial fan area in Taiwan,China to forecast groundwater level fluctuations.A hybrid imputation scheme is applied to reduce data errors and improve input continuity,including Z-score anomaly detection,sliding window segmentation,and STL-SARIMA-based imputation.The prediction model employs the BiLSTM model combined with the Bayesian optimization algorithm,achieving an R2 of 0.9932 and consistently lower MSE values than those of the LSTM and RNN models across all experiments.Specifically,BiLSTM reduces MSE by 62.9%compared to LSTM and 72.6%compared to RNN,while also achieving the lowest MAE and MAPE scores,demonstrating its superior accuracy and robustness in groundwater level forecasting.This predictive advantage stems from the integration of a hybrid statistical imputation process with a BiLSTM model optimized through Bayesian search.These components collectively enable a reliable and integrated forecasting system that effectively models groundwater level variations,thereby providing a practical solution for groundwater monitoring and sustainable water resource management.
文摘This paper explores the possibility of using machine learning algorithms to predict type 2 diabetes.We selected two commonly used classification models:random forest and logistic regression,modeled patients’clinical and lifestyle data,and compared their prediction performance.We found that the random forest model achieved the highest accuracy,demonstrated excellent classification results on the test set,and better distinguished between diabetic and non-diabetic patients by the confusion matrix and other evaluation metrics.The support vector machine and logistic regression perform slightly less well but achieve a high level of accuracy.The experimental results validate the effectiveness of the three machine learning algorithms,especially random forest,in the diabetes prediction task and provide useful practical experience for the intelligent prevention and control of chronic diseases.This study promotes the innovation of the diabetes prediction and management model,which is expected to alleviate the pressure on medical resources,reduce the burden of social health care,and improve the prognosis and quality of life of patients.In the future,we can consider expanding the data scale,exploring other machine learning algorithms,and integrating multimodal data to further realize the potential of artificial intelligence(AI)in the field of diabetes.
基金supported by the National Natural Science Foundation of China (Grant No. 91437113)the Special Fund for Meteorological Scientific Research in the Public Interest (Grant Nos. GYHY201506007 and GYHY201006015)+1 种基金the National 973 Program of China (Grant Nos. 2012CB417204 and 2012CB955200)the Scientific Research & Innovation Projects for Academic Degree Students of Ordinary Universities of Jiangsu (Grant No. KYLX 0827)
文摘An initial conditions （ICs） perturbation method was developed with the aim to improve an operational regional ensemble prediction system （REPS）. Three issues were identified and investigated： （1） the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; （2） the scale characteristic of the IC perturbations of the REPS; and （3） whether the REPS＇s skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system （GEPS） were analyzed. A ＂multi-scale blending＂ （MSB） IC perturbation scheme was developed, and the main findings can be summarized as follows： The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification.
基金supported by the National Science and Technology Support Program(Grant.No.2012BAC22B03)the National Natural Science Foundation of China(Grant No.41475100)+1 种基金the Youth Innovation Promotion Association of Chinese Academy of Sciencesthe Japan Society for the Promotion of Science KAKENHI(Grant.No.26282111)
文摘This paper investigates the possible sources of errors associated with tropical cyclone （TC） tracks forecasted using the Global/Regional Assimilation and Prediction System （GRAPES）. The GRAPES forecasts were made for 16 landfaIling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions （＂initials＂, hereafter）, which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.
基金The National Natural Science Foundation of China under contract No.41690124the Scientific Research Fund of the Second Institute of Oceanography,Ministry of Natural Resources under contract No.JG2007+1 种基金the National Natural Science Foundation of China under contract Nos 42006034,41690120 and 41530961the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.311021009.
文摘A new nudging scheme is proposed for the operational prediction system of the National Marine Environmental Forecasting Center(NMEFC)of China,mainly aimed at improving El Niño–Southern Oscillation(ENSO)and Indian Ocean Dipole(IOD)predictions.Compared with the origin nudging scheme of NMEFC,the new scheme adds a nudge assimilation for wind components,and increases the nudging weight at the subsurface.Increasing the nudging weight at the subsurface directly improved the simulation performance of the ocean component,while assimilating low-level wind components not only affected the atmospheric component but also benefited the oceanic simulation.Hindcast experiments showed that the new scheme remarkably improved both ENSO and IOD prediction skills.The skillful prediction lead time of ENSO was up to 11 months,1 month longer than a hindcast using the original nudging scheme.Skillful prediction of IOD could be made 4–5 months ahead by the new scheme,with a 0.2 higher correlation at a 3-month lead time.These prediction skills approach the level of some of the best state-of-the-art coupled general circulation models.Improved ENSO and IOD predictions occurred across all seasons,but mainly for target months in the boreal spring for the ENSO and the boreal spring and summer for the IOD.
基金supported by the National Key Research and Development Program of China(Grant Nos.2017YFC1404105,2017YFC1404100,2017YFC1404101,2017YFC1404102,2017YFC1404103 and 2017YFC1404104)
文摘The‘Two Oceans and One Sea’area(West Pacific,Indian Ocean,and South China Sea;15°S–60°N,39°–178°E)is a core strategic area for the‘21st Century Maritime Silk Road’project,as well as national defense.With the increasing demand for disaster prevention and mitigation,the importance of 10–30-day extended range prediction,between the conventional short-term(around seven days)and the climate scale(longer than one month),is apparent.However,marine extended range prediction is still a‘blank point’in China,making the early warning of marine disasters almost impossible.Here,the authors introduce a recently launched Chinese national project on a numerical forecasting system for extended range prediction in the‘Two Oceans and One Sea’area based on a regional ultra-high resolution multi-layer coupled model,including the scientific aims,technical scheme,innovation,and expected achievements.The completion of this prediction system is of considerable significance for the economic development and national security of China.
基金supported by the National Key Research and Development (R&D) Program of the Ministry of Science and Technology of China (Grant No. 2021YFC3000902)
文摘How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts.In this study,a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System(GRAPES)Convection-Allowing Ensemble Prediction System(CAEPS).The nonlinear forcing singular vector(NFSV)approach,that is,conditional nonlinear optimal perturbation-forcing(CNOP-F),is applied in this study,to construct a nonlinear model perturbation method for GRAPES-CAEPS.Three experiments are performed:One of them is the CTL experiment,without adding any model perturbation;the other two are NFSV-perturbed experiments,which are perturbed by NFSV with two different groups of constraint radii to test the sensitivity of the perturbation magnitude constraint.Verification results show that the NFSV-perturbed experiments achieve an overall improvement and produce more skillful forecasts compared to the CTL experiment,which indicates that the nonlinear NFSV-perturbed method can be used as an effective model perturbation method for convection-scale ensemble forecasts.Additionally,the NFSV-L experiment with large perturbation constraints generally performs better than the NFSV-S experiment with small perturbation constraints in the verification for upper-air and surface weather variables.But for precipitation verification,the NFSV-S experiment performs better in forecasts for light precipitation,and the NFSV-L experiment performs better in forecasts for heavier precipitation,indicating that for different precipitation events,the perturbation magnitude constraint must be carefully selected.All the findings above lay a foundation for the design of nonlinear model perturbation methods for future CAEPSs.
基金jointly supported by the National Basic Research Program of China (Grant No.2009CB421407)the National Key Technologies R&D Program of China (Grant Nos.2007BAC29B03 and 2006BAC02B04)the National Natural Science Foundation of China (Grant No.40605023)
文摘The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Physics Dynamical Seasonal Prediction System (IAP DCP),along with the National Centers for Environmental Prediction (NCEP) reanalysis data from the period of 1980-2000.The large-scale characteristics of the SCSSM monthly and seasonal mean low-level circulation have been well reproduced by IAP DCP,especially for the zonal wind at 850 hPa;furthermore,the hindcast variability also agrees quite well with observations.By introducing the South China Sea summer monsoon index,the potential predictability of IAP DCP for the intensity of the SCSSM has been evaluated.IAP DCP showed skill in predicting the interannual variation of SCSSM intensity.The result is highly encouraging;the correlation between the hindcasted and observed SCSSM Index was 0.58,which passes the 95% significance test.The result for the seasonal mean June-July-August SCSSM Index was better than that for the monthly mean,suggesting that seasonal forecasts are more reliable than monthly forecasts.
基金supported by the National Natural Science Foundation of China under Grant No. 61175007the National Key Technologies R&D Program under Grant No. 2012BAH07B01the National Key Basic Research Program of China (973 Program) under Grant No. 2012CB316302
文摘Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.
基金funded by the Na-tional Natural Science Foundation of China[grant number 42005117]the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDB40030205]the Key Special Project for the Introducing Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory(Guangdong)[grant number GML2019ZD0601]。
文摘The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the prediction skill of the system at a 10-day lead time for monthly TC activity is given based on 35-year(1981–2015)hindcasts with 24 ensemble members.The results show that FGOALS-f2 can capture the climatology of TC track densities in each month,but there is a delay in the monthly southward movement in the area of high track densities of TCs.The temporal correlation coefficient of TC frequency fluctuates across the different months,among which the highest appears in October(0.59)and the lowest in August(0.30).The rank correlation coefficients of TC track densities are relatively higher(R>0.6)in July,September,and November,while those in August and October are relatively lower(R within 0.2 to 0.6).For real-time prediction of TCs in 2020(July to November),FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement.Besides,the system successfully forecasts the correct sign of monthly anomalies of TC frequency and accumulated cyclone energy for 2020(July to November)in the SCS.
基金This work was supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038+2 种基金in part by the innovation practice project of college students in Heilongjiang Province under Grant Nos.202010231009,202110231024,and 202110231155in part by the basic scientific research business expenses scientific research projects of provincial universities in Heilongjiang Province Grant Nos.XJGZ2021001in part by the Education and teaching reform program of 2021 in Heilongjiang Province under Grant No.SJGY20210457.
文摘Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.
基金supported by the Special Fund for Meteorology Scientific Research in the Public Interest in 2007(GYHY(QX)2007-6-12)
文摘Based on an Ensemble Prediction System with the BGM method on the regional numerical prediction model AREM, Static State Perturbation （SSP, including Initial Random Perturbation and Perturbation Restriction） and Dynamic State Perturbation （DSP） are designed. The impacts of both perturbations on precipitation prediction are studied by analyzing a slrong precipitation process oc- curring during July 20-21, 2008. The results show that both SSP and DSP play a positive role in prediction of mesoscale precipita- tion, such as lowering the （missing） rate of precipitation prediction. SSP is mainly helpful for the 24-hour prediction, while DSP can improve both 24-hour and 48-hour prediction. DSP is better than the two SSPs in the hit rate of regional precipitation prediction. However, the former also has a little higher false alarm rate than the latter. DSP enlarges in some extent the dispersion of EPS, which is good for EPS.
文摘Objective: Analyzing 6 biomarkers, such as procalcitonin (PCT), C-reactive protein (CRP), fibrinogen (Fib), lactate concentration (Lac), D-dimer (D-d), neutrophil ratio (NEUT%) to figure out several sensitive indicators and establish a new prediction system of sepsis, which could achieve a higher sensitivity and specificity to predict sepsis. Methods: We collected 113 SIRS patients in ICU. According to their prognosis, all the patients were divided into two groups named sepsis and non-sepsis group according to the diagnostic criteria of sepsis. We recorded the general information and detected the plasma levels of the 6 biomarkers. Results: The plasma levels of NEUT% and Fib between the two groups had no significant difference. PCT had the highest prediction accuracy of sepsis compared with other biomarkers. A predictive model was established, in which Lac, PCT, CRP were enrolled. The final prediction model was: logit(P) = 0.314 + 0.105 × Lac(mmol/l) + 0.099 × PCT(ng/mL) + 0.012 × CRP(mg/L). The area under the curve of the prediction model was 0.893, which was higher than every single biomarker involved in this study. Conclusion: The three serum biomarkers of Lac, PCT, CRP are used to establish a prediction model of sepsis: logit(P) = 0.314 + 0.105 × Lac(mmol/l) + 0.099 × PCT(ng/mL) + 0.012 × CRP(mg/L), which can better predict the occurrence of sepsis compared with other biomarkers.
文摘A soft sensing method of burning through point (BTP) was described and a new predictive parameter—the mathematics inflexion point of waste gas temperature curve in the middle of the strand was proposed. The artificial neural network was used in predicting BTP, modification on backpropagation algorithm was made in order to improve the convergence and self organize the hidden layer neurons. The adaptive prediction system developed on these techniques shows its characters such as fast, accuracy, less dependence on production data. The prediction of BTP can be used as operation guidance or control parameter.[
基金This work was jointly supported by the National Natural Science Foundation of China[grant numbers 41521004 and 41875083]the Gansu Provincial Special Fund Project for Guiding Scientific and Technological Innovation and Development[grant number 2019ZX-06].
文摘In 2020,the COVID-19 pandemic spreads rapidly around the world.To accurately predict the number of daily new cases in each country,Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic(GPCP).In this article,the authors use the ensemble empirical mode decomposition(EEMD)model and autoregressive moving average(ARMA)model to improve the prediction results of GPCP.In addition,the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease,whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model.Judging from the results,the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP.For countries such as El Salvador with a small number of cases,the absolute values of the relative errors of prediction become smaller.Therefore,this article concludes that this method is more effective for improving prediction results and direct prediction.
基金jointly supported by the National Key Research and Development Program of China (Grant. No. 2017YFC1501601)the National Natural Science Foundation of China (Grant. No. 41475100)+1 种基金the National Science and Technology Support Program (Grant. No. 2012BAC22B03)the Youth Innovation Promotion Association of the Chinese Academy of Sciences
文摘This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II.Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60?S and 60?N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.
