Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data.However,many sources of uncertainty affect above-ground biomass estimation and are challenging ...Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data.However,many sources of uncertainty affect above-ground biomass estimation and are challenging to assess.In this study,the uncertainties associated with the measurement error in independent variables(diameter at breast height,tree height),residual variability,variances of the parameter estimates,and the sampling variability of national inventory data are estimated for five above-ground biomass models.The results show sampling variability is the most significant source of uncertainty.The measurement error and residual variability have negligible effects on forests above-ground biomass estimations.Thus,a reduction in the uncertainty of the sampling variability has the greatest potential to decrease the overall uncertainty.The power model containing only the diameter at breast height has the smallest uncertainty.The findings of this study provide suggestions to achieve a trade-off between accuracy and cost for above-ground biomass estimation using field work.展开更多
Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and ...Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS(Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.展开更多
基金supported financially by the National Key R&D Program of China(Grant No.2017YFC0506503-02)。
文摘Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data.However,many sources of uncertainty affect above-ground biomass estimation and are challenging to assess.In this study,the uncertainties associated with the measurement error in independent variables(diameter at breast height,tree height),residual variability,variances of the parameter estimates,and the sampling variability of national inventory data are estimated for five above-ground biomass models.The results show sampling variability is the most significant source of uncertainty.The measurement error and residual variability have negligible effects on forests above-ground biomass estimations.Thus,a reduction in the uncertainty of the sampling variability has the greatest potential to decrease the overall uncertainty.The power model containing only the diameter at breast height has the smallest uncertainty.The findings of this study provide suggestions to achieve a trade-off between accuracy and cost for above-ground biomass estimation using field work.
基金supported by the National High Technology Research and Development Program of China(863 Program)(No.2015AA050204)
文摘Some research work has showed that public mood and stock market price have some relations in some degree. Although it is difficult to clear the relation, the research about the relation between stock market price and public mood is interested by some scientists. This paper tries to find the relationship between Chinese stock market and Chinese local Microblog. First, C-POMS(Chinese Profile of Mood States) was proposed to analyze sentiment of Microblog feeds. Then Granger causality test confirmed the relation between C-POMS analysis and price series. SVM and Probabilistic Neural Network were used to make prediction, and experiments show that SVM is better to predict stock market movements than Probabilistic Neural Network. Experiments also indicate that adding certain dimension of C-POMS as the input data will improve the prediction accuracy to 66.667%. Two dimensions to input data leads to the highest accuracy of 71.429%, which is about 20% higher than using only history stock data as the input data. This paper also compared the proposed method with the ROSTEA scores, and concluded that only the proposed method brings more accurate predicts.
