The time forecast system of coal spontaneous combustion was described based on the KJ-90 mine environmental monitoring system in Hulipo Mine, Luzhou, Sichuan Province. In the system, CO and O2 sensor are added, and sp...The time forecast system of coal spontaneous combustion was described based on the KJ-90 mine environmental monitoring system in Hulipo Mine, Luzhou, Sichuan Province. In the system, CO and O2 sensor are added, and special-purpose microcomputer and software are equipped. By means of the system, the fluctuation laws of fire forecast parameters were observed in the process of coal-cutting, blast, exogenous mine fire and working face air quantity variation. This paper puts forward data processing method based on the Raita method.展开更多
A new and useful method of technology economics, parameter estimation method, was presented in light of the stability of gravity center of object in this paper. This method could deal with the fitting and forecasting ...A new and useful method of technology economics, parameter estimation method, was presented in light of the stability of gravity center of object in this paper. This method could deal with the fitting and forecasting of economy volume and could greatly decrease the errors of the fitting and forecasting results. Moreover, the strict hypothetical conditions in least squares method were not necessary in the method presented in this paper, which overcame the shortcomings of least squares method and expanded the application of data barycentre method. Application to the steel consumption volume forecasting was presented in this paper. It was shown that the result of fitting and forecasting was satisfactory. From the comparison between data barycentre forecasting method and least squares method, we could conclude that the fitting and forecasting results using data barycentre method were more stable than those of using least squares regression forecasting method, and the computation of data barycentre forecasting method was simpler than that of least squares method. As a result, the data barycentre method was convenient to use in technical economy.展开更多
Forecasting environmental parameters in the distant future requires complex modelling and large computational resources.Due to the sensitivity and complexity of forecast models,long-term parameter forecasts(e.g.up to ...Forecasting environmental parameters in the distant future requires complex modelling and large computational resources.Due to the sensitivity and complexity of forecast models,long-term parameter forecasts(e.g.up to 2100)are uncommon and only produced by a few organisations,in heterogeneous formats and based on different assumptions of greenhouse gases emissions.However,data mining techniques can be used to coerce the data to a uniform time and spatial representation,which facilitates their use in many applications.In this paper,streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to(i)standardise and harmonise the data representations,(ii)produce intermediate scenarios and new informative parameters,and(iii)align all sets on a common time and spatial resolution.Time series crosscorrelation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas.Our results highlight that(i)the Mediterranean Sea may have a standalone‘response’to climate change with respect to other areas,(ii)the Poles are most representative of global forecasted change,and(iii)the trends are generally alarming for most oceans.展开更多
Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions b...Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.展开更多
基金Supported by National Natural Science Foundation of China (50274061) and National High Technology Research and Development Program of China(2003AA131100-02-06)
文摘The time forecast system of coal spontaneous combustion was described based on the KJ-90 mine environmental monitoring system in Hulipo Mine, Luzhou, Sichuan Province. In the system, CO and O2 sensor are added, and special-purpose microcomputer and software are equipped. By means of the system, the fluctuation laws of fire forecast parameters were observed in the process of coal-cutting, blast, exogenous mine fire and working face air quantity variation. This paper puts forward data processing method based on the Raita method.
文摘A new and useful method of technology economics, parameter estimation method, was presented in light of the stability of gravity center of object in this paper. This method could deal with the fitting and forecasting of economy volume and could greatly decrease the errors of the fitting and forecasting results. Moreover, the strict hypothetical conditions in least squares method were not necessary in the method presented in this paper, which overcame the shortcomings of least squares method and expanded the application of data barycentre method. Application to the steel consumption volume forecasting was presented in this paper. It was shown that the result of fitting and forecasting was satisfactory. From the comparison between data barycentre forecasting method and least squares method, we could conclude that the fitting and forecasting results using data barycentre method were more stable than those of using least squares regression forecasting method, and the computation of data barycentre forecasting method was simpler than that of least squares method. As a result, the data barycentre method was convenient to use in technical economy.
基金This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the BlueBRIDGE project[grant agreement no 675680].
文摘Forecasting environmental parameters in the distant future requires complex modelling and large computational resources.Due to the sensitivity and complexity of forecast models,long-term parameter forecasts(e.g.up to 2100)are uncommon and only produced by a few organisations,in heterogeneous formats and based on different assumptions of greenhouse gases emissions.However,data mining techniques can be used to coerce the data to a uniform time and spatial representation,which facilitates their use in many applications.In this paper,streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to(i)standardise and harmonise the data representations,(ii)produce intermediate scenarios and new informative parameters,and(iii)align all sets on a common time and spatial resolution.Time series crosscorrelation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas.Our results highlight that(i)the Mediterranean Sea may have a standalone‘response’to climate change with respect to other areas,(ii)the Poles are most representative of global forecasted change,and(iii)the trends are generally alarming for most oceans.
基金supported by the Special Fund for Meteorological Scientific Research in the Public Interest (Grant No. GYHY201506002, CRA40: 40-year CMA global atmospheric reanalysis)the National Basic Research Program of China (Grant No. 2015CB953703)+1 种基金the Intergovernmental Key International S & T Innovation Cooperation Program (Grant No. 2016YFE0102400)the National Natural Science Foundation of China (Grant Nos. 41305052 & 41375139)
文摘Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.
