The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support v...The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau’s as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.展开更多
Monitoring and evaluating the nutritional status of vegetation under stress from exhausted coal mining sites by hyper-spectral remote sensing is important in future ecological restoration engineering. The Wangpingcun ...Monitoring and evaluating the nutritional status of vegetation under stress from exhausted coal mining sites by hyper-spectral remote sensing is important in future ecological restoration engineering. The Wangpingcun coal mine, located in the Mentougou district of Beijing, was chosen as a case study. The ecological damage was analyzed by 3S technology, field investigation and from chemical data. The derivative spectra of the diagnostic absorption bands are derived from the spectra measured in the field and used as characteristic spectral variables. A correlation analysis was conducted for the nitrogen content of the vegetation samples and the fast derivative spectrum and the estimation model of nitrogen content established by a multiple stepwise linear regression method. The spatial distribution of nitrogen content was extracted by a parameter mapping method from the Hyperion data which revealed the distribution of the nitrogen content. In addition, the estimation model was evaluated for two evaluation indicators which are important for the precision of the model. Experimental results indicate that by linear regression and parameter mapping, the estimation model precision was Very high. The coefficient of determination, R2, was 0.795 and the standard deviation of residual （SDR） 0.19. The nitrogen content of most samples was about 1.03% and the nitrogen content in the study site seems inversely proportional to the distance from the piles of coal waste. Therefore, we can conclude that inversely modeling nitrogen content by hyper-spectral remote sensing in exhausted coal mining sites is feasible and our study can be taken as reference in species selection and in subseauent management and maintenance in ecological restoration.展开更多
基金Project 072400430420 supported by the Natural Science Foundation of Henan Province
文摘The basic principles of the Support Vector Machine (SVM) are introduced in this paper. A specific process to establish an SVM prediction model is given. To improve the precision of coal reserve estimation, a support vector machine method, based on statistical learning theory, is put forward. The SVM model was trained and tested by using the existing exploration and exploitation data of Chencun mine of Yima bureau’s as the input data. Then coal reserves within a particular region were calculated. These calculated results and the actual results of the exploration block were compared. The maximum relative error was 10.85%, within the scope of acceptable error limits. The results show that the SVM coal reserve calculation method is reliable. This method is simple, practical and valuable.
文摘Monitoring and evaluating the nutritional status of vegetation under stress from exhausted coal mining sites by hyper-spectral remote sensing is important in future ecological restoration engineering. The Wangpingcun coal mine, located in the Mentougou district of Beijing, was chosen as a case study. The ecological damage was analyzed by 3S technology, field investigation and from chemical data. The derivative spectra of the diagnostic absorption bands are derived from the spectra measured in the field and used as characteristic spectral variables. A correlation analysis was conducted for the nitrogen content of the vegetation samples and the fast derivative spectrum and the estimation model of nitrogen content established by a multiple stepwise linear regression method. The spatial distribution of nitrogen content was extracted by a parameter mapping method from the Hyperion data which revealed the distribution of the nitrogen content. In addition, the estimation model was evaluated for two evaluation indicators which are important for the precision of the model. Experimental results indicate that by linear regression and parameter mapping, the estimation model precision was Very high. The coefficient of determination, R2, was 0.795 and the standard deviation of residual （SDR） 0.19. The nitrogen content of most samples was about 1.03% and the nitrogen content in the study site seems inversely proportional to the distance from the piles of coal waste. Therefore, we can conclude that inversely modeling nitrogen content by hyper-spectral remote sensing in exhausted coal mining sites is feasible and our study can be taken as reference in species selection and in subseauent management and maintenance in ecological restoration.
