Based on the monitoring data of chemical oxygen demand(COD),permanganate index(I Mn)and five-day biochemical oxygen demand(BOD 5)of surface water in Tongling section of Yangtze River,the linear relationship among the ...Based on the monitoring data of chemical oxygen demand(COD),permanganate index(I Mn)and five-day biochemical oxygen demand(BOD 5)of surface water in Tongling section of Yangtze River,the linear relationship among the three indexes in the annual data analysis and the internal reasons,as well as the linear relationship and changes among the three indexes in different seasons were analyzed.The results reveal that in terms of the whole year,COD,I Mn and BOD 5 had a significant correlation and good linear relationship.The fitting slopes of the three indexes were 3.89 of COD/I Mn,4.39 of COD/BOD 5 and 1.16 of I Mn/BOD 5,respectively,which corresponded to the proportional relationship among the three indexes.From the perspective of seasonal changes,there was a very significant correlation between the three indexes in spring and summer.In autumn and winter,only COD and I Mn had a good correlation,but they had a poor correlation with BOD 5.展开更多
Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This pap...Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.展开更多
The earthquake is considered one of the most devastating disasters in any area of the world due to its potentially destructive force.Based on the various earthquake-related parameters,the risk assessment is enabled in...The earthquake is considered one of the most devastating disasters in any area of the world due to its potentially destructive force.Based on the various earthquake-related parameters,the risk assessment is enabled in advance to prevent future earthquake disasters.In this paper,for providing the shelter space demands to reduce the damage level and prevention costs,an earthquake risk assessment approach is proposed for deriving the risk index based on multiple spatial parameters in the gridded map.The proposed assessment approach is comprised of pre-processing,methodologymodel,and data visualization.The risk index model derives the earthquake risk index by multiple spatial parameters including indexes of earthquake,danger,shelter,and building for blocks in the quantitative gridded map.The parameters are provided based onmathematicalmodels and combinedwith the risk index that presents the earthquake risk assessment result for each block.Therefore,the gridding approach is proposed to provide the elements of the risk assessment area that are used in the spatial parameters.The gridded map is developed for the selected area to visualize risk index parameters associated with each risk zone.Based on the derived result of the proposed earthquake risk indexmodel,emergency shelter requirements are provided according to the risk index for each location,which supports safety measures in advance to prevent future earthquake disasters.展开更多
One of the vital components of the macroeconomic model that helps policymaking is the demand for money function.Having reliable predictions on the money demand function helps in determining the optimum growth of money...One of the vital components of the macroeconomic model that helps policymaking is the demand for money function.Having reliable predictions on the money demand function helps in determining the optimum growth of money supply which is vital in controlling the inflation rate in the economy and also preventing monetary disturbances from affecting real output.In order to formulate and estimate the money demand function in Ethiopia,this study used quarterly data from 2000Q3 to 2021Q2 and employed the Ordinary Least Square method and Engle-Granger two-stage procedure for empirical analysis.The empirical result from the models indicates that,in the long run,all variables(real GDP,CPI inflation,real effective exchange rate,real interest rate and lagged real money balance)are significantly affecting the demand for money in Ethiopia.Whereas,the estimated coefficients of the short-run variable show that the real effective exchange rate,CPI inflation,and lagged real money balance are the main determinants of demand for money while the real GDP and real interest rate are insignificant.Another important finding is that absolute value of the coefficient of the error correction term implies that about 54.2%of the disequilibrium in real money demand is counter-balanced by short-run adjustment in each quarter.The study suggests that in conducting monetary policy,policymakers should consider not only the behavior of income and price but also the movement of exchange rates.The study also calls for appropriate formulation and estimation of the all-encompassing demand for money function that is capable of bringing stability to the growth of money coupled with sustainable economic growth.展开更多
文摘Based on the monitoring data of chemical oxygen demand(COD),permanganate index(I Mn)and five-day biochemical oxygen demand(BOD 5)of surface water in Tongling section of Yangtze River,the linear relationship among the three indexes in the annual data analysis and the internal reasons,as well as the linear relationship and changes among the three indexes in different seasons were analyzed.The results reveal that in terms of the whole year,COD,I Mn and BOD 5 had a significant correlation and good linear relationship.The fitting slopes of the three indexes were 3.89 of COD/I Mn,4.39 of COD/BOD 5 and 1.16 of I Mn/BOD 5,respectively,which corresponded to the proportional relationship among the three indexes.From the perspective of seasonal changes,there was a very significant correlation between the three indexes in spring and summer.In autumn and winter,only COD and I Mn had a good correlation,but they had a poor correlation with BOD 5.
文摘Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.
基金This research was supported in part by the Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073387)in part by the Basic Science Research Program through the NRF funded by the Ministry of Education(NRF-2019R1I1A1A01062456),Any correspondence related to this paper should be addressed to Dohyeun Kim.
文摘The earthquake is considered one of the most devastating disasters in any area of the world due to its potentially destructive force.Based on the various earthquake-related parameters,the risk assessment is enabled in advance to prevent future earthquake disasters.In this paper,for providing the shelter space demands to reduce the damage level and prevention costs,an earthquake risk assessment approach is proposed for deriving the risk index based on multiple spatial parameters in the gridded map.The proposed assessment approach is comprised of pre-processing,methodologymodel,and data visualization.The risk index model derives the earthquake risk index by multiple spatial parameters including indexes of earthquake,danger,shelter,and building for blocks in the quantitative gridded map.The parameters are provided based onmathematicalmodels and combinedwith the risk index that presents the earthquake risk assessment result for each block.Therefore,the gridding approach is proposed to provide the elements of the risk assessment area that are used in the spatial parameters.The gridded map is developed for the selected area to visualize risk index parameters associated with each risk zone.Based on the derived result of the proposed earthquake risk indexmodel,emergency shelter requirements are provided according to the risk index for each location,which supports safety measures in advance to prevent future earthquake disasters.
文摘One of the vital components of the macroeconomic model that helps policymaking is the demand for money function.Having reliable predictions on the money demand function helps in determining the optimum growth of money supply which is vital in controlling the inflation rate in the economy and also preventing monetary disturbances from affecting real output.In order to formulate and estimate the money demand function in Ethiopia,this study used quarterly data from 2000Q3 to 2021Q2 and employed the Ordinary Least Square method and Engle-Granger two-stage procedure for empirical analysis.The empirical result from the models indicates that,in the long run,all variables(real GDP,CPI inflation,real effective exchange rate,real interest rate and lagged real money balance)are significantly affecting the demand for money in Ethiopia.Whereas,the estimated coefficients of the short-run variable show that the real effective exchange rate,CPI inflation,and lagged real money balance are the main determinants of demand for money while the real GDP and real interest rate are insignificant.Another important finding is that absolute value of the coefficient of the error correction term implies that about 54.2%of the disequilibrium in real money demand is counter-balanced by short-run adjustment in each quarter.The study suggests that in conducting monetary policy,policymakers should consider not only the behavior of income and price but also the movement of exchange rates.The study also calls for appropriate formulation and estimation of the all-encompassing demand for money function that is capable of bringing stability to the growth of money coupled with sustainable economic growth.
