Now environment is an important topic in academic field.Many researches focus on the negative outcomes of nature which are being continually created by human and much attention has been paid to how the environment is ...Now environment is an important topic in academic field.Many researches focus on the negative outcomes of nature which are being continually created by human and much attention has been paid to how the environment is protected through integrated research,movement and policy.But few studies are concentrated on population and environment and are to find out the interconnectivity and underlying mechanism that have an effect on people's preferring additional children and perception to environment.Many researchers claim that population growth is a great problem for environment but they do not provide the indepth integrated knowledge and mechanism that influence fertility trend and environmental problems.It is very crucial to develop practical and concrete initiatives to confirm a transition to reduce population growth and redirect the perception on population and environment.展开更多
The ongoing research for model choice and selection has generated a plethora of approaches. With such a wealth of methods, it can be difficult for a researcher to know what model selection approach is the proper w...The ongoing research for model choice and selection has generated a plethora of approaches. With such a wealth of methods, it can be difficult for a researcher to know what model selection approach is the proper way to proceed to select the appropriate model for prediction. The authors present an evaluation of various model selection criteria from decision-theoretic perspective using experimental data to define and recommend a criterion to select the best model. In this analysis, six of the most common selection criteria, nineteen friction factor correlations, and eight sets of experimental data are employed. The results show that while the use of the traditional correlation coefficient, R2 is inappropriate, root mean square error, RMSE can be used to rank models, but does not give much insight on their accuracy. Other criteria such as correlation ratio, mean absolute error, and standard deviation are also evaluated. The AIC （Akaike Information Criterion） has shown its superiority to other selection criteria. The authors propose AIC as an alternative to use when fitting experimental data or evaluating existing correlations. Indeed, the AIC method is an information theory based, theoretically sound and stable. The paper presents a detailed discussion of the model selection criteria, their pros and cons, and how they can be utilized to allow proper comparison of different models for the best model to be inferred based on sound mathematical theory. In conclusion, model selection is an interesting problem and an innovative strategy to help alleviate similar challenges faced by the professionals in the oil and gas industry is introduced.展开更多
文摘Now environment is an important topic in academic field.Many researches focus on the negative outcomes of nature which are being continually created by human and much attention has been paid to how the environment is protected through integrated research,movement and policy.But few studies are concentrated on population and environment and are to find out the interconnectivity and underlying mechanism that have an effect on people's preferring additional children and perception to environment.Many researchers claim that population growth is a great problem for environment but they do not provide the indepth integrated knowledge and mechanism that influence fertility trend and environmental problems.It is very crucial to develop practical and concrete initiatives to confirm a transition to reduce population growth and redirect the perception on population and environment.
文摘The ongoing research for model choice and selection has generated a plethora of approaches. With such a wealth of methods, it can be difficult for a researcher to know what model selection approach is the proper way to proceed to select the appropriate model for prediction. The authors present an evaluation of various model selection criteria from decision-theoretic perspective using experimental data to define and recommend a criterion to select the best model. In this analysis, six of the most common selection criteria, nineteen friction factor correlations, and eight sets of experimental data are employed. The results show that while the use of the traditional correlation coefficient, R2 is inappropriate, root mean square error, RMSE can be used to rank models, but does not give much insight on their accuracy. Other criteria such as correlation ratio, mean absolute error, and standard deviation are also evaluated. The AIC （Akaike Information Criterion） has shown its superiority to other selection criteria. The authors propose AIC as an alternative to use when fitting experimental data or evaluating existing correlations. Indeed, the AIC method is an information theory based, theoretically sound and stable. The paper presents a detailed discussion of the model selection criteria, their pros and cons, and how they can be utilized to allow proper comparison of different models for the best model to be inferred based on sound mathematical theory. In conclusion, model selection is an interesting problem and an innovative strategy to help alleviate similar challenges faced by the professionals in the oil and gas industry is introduced.
