Liaocheng Prefecture is located in North China Plain with a long reclamation history of more than 10000 years. In this study the author applied data to explain the relationship between land use pattern and physical, s...Liaocheng Prefecture is located in North China Plain with a long reclamation history of more than 10000 years. In this study the author applied data to explain the relationship between land use pattern and physical, social and economic factors and further to find out driving forces which lead to land use changes in such an agricultural region. Data of three different time points on township level were taken into account to explain the land use pattern, and land use changes. And 40-year county level data were applied to analyze the driving forces. Canonical Correlation Analysis was conducted to explain the relationship between land use pattern and social and economic factors; and Linear Regression Analysis was used to find out driving forces of land use change, thus to project the future trend of land use change in Liaocheng Prefecture.展开更多
Inferential models are widely used in the chemical industry to infer key process variables, which are challenging or expensive to measure, from other more easily measured variables. The aim of this paper is three-fold...Inferential models are widely used in the chemical industry to infer key process variables, which are challenging or expensive to measure, from other more easily measured variables. The aim of this paper is three-fold: to present a theoretical review of some of the well known linear inferential modeling techniques, to enhance the predictive ability of the regularized canonical correlation analysis (RCCA) method, and finally to compare the performances of these techniques and highlight some of the practical issues that can affect their predictive abilities. The inferential modeling techniques considered in this study include full rank modeling techniques, such as ordinary least square (OLS) regression and ridge regression (RR), and latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS) regression, and regularized canonical correlation analysis (RCCA). The theoretical analysis shows that the loading vectors used in LVR modeling can be computed by solving eigenvalue problems. Also, for the RCCA method, we show that by optimizing the regularization parameter, an improvement in prediction accuracy can be achieved over other modeling techniques. To illustrate the performances of all inferential modeling techniques, a comparative analysis was performed through two simulated examples, one using synthetic data and the other using simulated distillation column data. All techniques are optimized and compared by computing the cross validation mean square error using unseen testing data. The results of this comparative analysis show that scaling the data helps improve the performances of all modeling techniques, and that the LVR techniques outperform the full rank ones. One reason for this advantage is that the LVR techniques improve the conditioning of the model by discarding the latent variables (or principal components) with small eigenvalues, which also reduce the effect of the noise on the model prediction. The results also show that PCR and PLS have comparable performances, and that RCCA can provide an advantage by optimizing its regularization parameter.展开更多
基金Under the auspices of the National Natural Science Foundation of China (No. 49731020) and key project of Chinese Academy of Scie
文摘Liaocheng Prefecture is located in North China Plain with a long reclamation history of more than 10000 years. In this study the author applied data to explain the relationship between land use pattern and physical, social and economic factors and further to find out driving forces which lead to land use changes in such an agricultural region. Data of three different time points on township level were taken into account to explain the land use pattern, and land use changes. And 40-year county level data were applied to analyze the driving forces. Canonical Correlation Analysis was conducted to explain the relationship between land use pattern and social and economic factors; and Linear Regression Analysis was used to find out driving forces of land use change, thus to project the future trend of land use change in Liaocheng Prefecture.
文摘Inferential models are widely used in the chemical industry to infer key process variables, which are challenging or expensive to measure, from other more easily measured variables. The aim of this paper is three-fold: to present a theoretical review of some of the well known linear inferential modeling techniques, to enhance the predictive ability of the regularized canonical correlation analysis (RCCA) method, and finally to compare the performances of these techniques and highlight some of the practical issues that can affect their predictive abilities. The inferential modeling techniques considered in this study include full rank modeling techniques, such as ordinary least square (OLS) regression and ridge regression (RR), and latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS) regression, and regularized canonical correlation analysis (RCCA). The theoretical analysis shows that the loading vectors used in LVR modeling can be computed by solving eigenvalue problems. Also, for the RCCA method, we show that by optimizing the regularization parameter, an improvement in prediction accuracy can be achieved over other modeling techniques. To illustrate the performances of all inferential modeling techniques, a comparative analysis was performed through two simulated examples, one using synthetic data and the other using simulated distillation column data. All techniques are optimized and compared by computing the cross validation mean square error using unseen testing data. The results of this comparative analysis show that scaling the data helps improve the performances of all modeling techniques, and that the LVR techniques outperform the full rank ones. One reason for this advantage is that the LVR techniques improve the conditioning of the model by discarding the latent variables (or principal components) with small eigenvalues, which also reduce the effect of the noise on the model prediction. The results also show that PCR and PLS have comparable performances, and that RCCA can provide an advantage by optimizing its regularization parameter.
