The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that th...The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.展开更多
In order to analyze the characteristics of surface water resource quality for the reconstruction of old water treatment plant,multivariate statistical techniques such as cluster analysis and factor analysis were appli...In order to analyze the characteristics of surface water resource quality for the reconstruction of old water treatment plant,multivariate statistical techniques such as cluster analysis and factor analysis were applied to the data of Yuqiao Reservoir-surface water resource of the Luan River,China.The results of cluster analysis demonstrate that the months of one year were divided into 3 groups and the characteristic of clusters was agreed with the seasonal characteristics in North China.Three factors were derived from the complicated set using factor analysis.Factor 1 included turbidity and chlorophyll,which seemed to be related to the anthropogenic activities;factor 2 included alkaline and hardness,which were related to the natural characteristic of surface water;and factor 3 included Cl and NO2-N affected by mineral and agricultural activities.The sinusoidal shape of the score plots of the three factors shows that the temporal variations caused by natural and human factors are linked to seasonality.展开更多
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China （50579009, 70471090） the National 10 th Five Year Scientific Project of China for Tackling the Key Problems （2004BA608B-02 - 02） and the Excellence Youth Teacher Sustentation Fund Program of the Ministry of Education of China （Department of Education and Personnel [2002] 350）.
文摘The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.
基金Project supported by the Hi-Tech Research and Development(863)Program of China(No.2006AA06Z311)Development Program for Outstanding Young Teachers in Harbin Institute of Technology(No.HITQNJS.2008.044),China
文摘In order to analyze the characteristics of surface water resource quality for the reconstruction of old water treatment plant,multivariate statistical techniques such as cluster analysis and factor analysis were applied to the data of Yuqiao Reservoir-surface water resource of the Luan River,China.The results of cluster analysis demonstrate that the months of one year were divided into 3 groups and the characteristic of clusters was agreed with the seasonal characteristics in North China.Three factors were derived from the complicated set using factor analysis.Factor 1 included turbidity and chlorophyll,which seemed to be related to the anthropogenic activities;factor 2 included alkaline and hardness,which were related to the natural characteristic of surface water;and factor 3 included Cl and NO2-N affected by mineral and agricultural activities.The sinusoidal shape of the score plots of the three factors shows that the temporal variations caused by natural and human factors are linked to seasonality.
