Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimila...Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.展开更多
Model Output Statistics (MOS) is a well-known technique that allows improving outputs from numerical atmospheric models. In this contribution, we present the development of a MOS algorithm to improve air quality forec...Model Output Statistics (MOS) is a well-known technique that allows improving outputs from numerical atmospheric models. In this contribution, we present the development of a MOS algorithm to improve air quality forecasts in Catalonia, a region in the northeast of Spain. These forecasts are obtained from an Eulerian coupled air quality modelling system developed by Meteosim. Nitrogen Dioxide (NO2), Particulate Matter (PM10) and Ozone (03) have been the pollutants considered and the methodology has been applied on statistical values of these pollutants according to regulatory levels. Four MOS algorithms have been developed, characterized by different approaches in relation with seasonal stratification and stratification according to the measurement stations considered. Algorithms have been compared among them in order to obtain a MOS that reduces the forecast uncertainties. Results obtained show that the best MOS designed increases the accuracy of NO2 maximum 1-h daily value forecast from 71% to 75%, from 68% to 81% in the case of daily values of PM10, and finally, the accuracy of O3 maximum 1-h daily value from 79% to 87%.展开更多
The manufacturing industry is an important pillar of the national economy.It is of vital importance to develop statistical modellings in order to quantify the relationship between potential internal drivers and the tr...The manufacturing industry is an important pillar of the national economy.It is of vital importance to develop statistical modellings in order to quantify the relationship between potential internal drivers and the trend of output values in the manufacturing industry.However,only a few statistical modellings have been established to investigate such associations.This study developed the correlation coefficient model and generalized linear model(GLM)to measure the single and interactive effects of the internal drivers on the changes of the output values.For the GLM,different predictive variables were developed to fit into the dataset,and the performance of the models were compared using fitness parameters.Furthermore,an industry survey dataset for 1,180 manufacturing enterprises in 2020 was used to validate the models.The use of the GLM combining land area,number of employees,scientific research input,and labor productivity may have a great potential to bolster capacity in monitoring and predicting the trend of output values in the manufacture industry.展开更多
This study is aimed at the development of a statistical model for forecasting heavy rain in South Korea. For the 3-hour weather forecast system, the 10 km×10 km area-mean amount of rainfall at 6 stations （Seoul,...This study is aimed at the development of a statistical model for forecasting heavy rain in South Korea. For the 3-hour weather forecast system, the 10 km×10 km area-mean amount of rainfall at 6 stations （Seoul, Daejeon, Gangreung, （Jwangju, Busan, and Jeju） in South Korea are used. And the corresponding 45 synoptic factors generated by the numerical model are used as potential predictors. Four statistical forecast models （linear regression model, logistic regression model, neural network model and decision tree model） for the occurrence of heavy rain are based on the model output statistics （MOS） method. They are separately estimated by the same training data. The thresholds are considered to forecast the occurrence of heavy rain because the distribution of estimated values that are generated by each model is too skewed. The results of four models are compared via Heidke skill scores. As a result, the logistic regression model is recommended.展开更多
Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,wit...Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.展开更多
基金supported by the State Key Research and Development Program (Grant Nos. 2017YFC0209803, 2016YFC0208504, 2016YFC0203303 and 2017YFC0210106)the National Natural Science Foundation of China (Grant Nos. 91544230, 41575145, 41621005 and 41275128)
文摘Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.
基金This work was funded by the Catalan Government and the Spanish Government through the projects PTOP-2013-608 and PTQ-12-05244 respectively.
文摘Model Output Statistics (MOS) is a well-known technique that allows improving outputs from numerical atmospheric models. In this contribution, we present the development of a MOS algorithm to improve air quality forecasts in Catalonia, a region in the northeast of Spain. These forecasts are obtained from an Eulerian coupled air quality modelling system developed by Meteosim. Nitrogen Dioxide (NO2), Particulate Matter (PM10) and Ozone (03) have been the pollutants considered and the methodology has been applied on statistical values of these pollutants according to regulatory levels. Four MOS algorithms have been developed, characterized by different approaches in relation with seasonal stratification and stratification according to the measurement stations considered. Algorithms have been compared among them in order to obtain a MOS that reduces the forecast uncertainties. Results obtained show that the best MOS designed increases the accuracy of NO2 maximum 1-h daily value forecast from 71% to 75%, from 68% to 81% in the case of daily values of PM10, and finally, the accuracy of O3 maximum 1-h daily value from 79% to 87%.
文摘The manufacturing industry is an important pillar of the national economy.It is of vital importance to develop statistical modellings in order to quantify the relationship between potential internal drivers and the trend of output values in the manufacturing industry.However,only a few statistical modellings have been established to investigate such associations.This study developed the correlation coefficient model and generalized linear model(GLM)to measure the single and interactive effects of the internal drivers on the changes of the output values.For the GLM,different predictive variables were developed to fit into the dataset,and the performance of the models were compared using fitness parameters.Furthermore,an industry survey dataset for 1,180 manufacturing enterprises in 2020 was used to validate the models.The use of the GLM combining land area,number of employees,scientific research input,and labor productivity may have a great potential to bolster capacity in monitoring and predicting the trend of output values in the manufacture industry.
文摘This study is aimed at the development of a statistical model for forecasting heavy rain in South Korea. For the 3-hour weather forecast system, the 10 km×10 km area-mean amount of rainfall at 6 stations （Seoul, Daejeon, Gangreung, （Jwangju, Busan, and Jeju） in South Korea are used. And the corresponding 45 synoptic factors generated by the numerical model are used as potential predictors. Four statistical forecast models （linear regression model, logistic regression model, neural network model and decision tree model） for the occurrence of heavy rain are based on the model output statistics （MOS） method. They are separately estimated by the same training data. The thresholds are considered to forecast the occurrence of heavy rain because the distribution of estimated values that are generated by each model is too skewed. The results of four models are compared via Heidke skill scores. As a result, the logistic regression model is recommended.
基金from any funding agency in the public,commercial,or not-for-profit sectors.
文摘Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.
