In this paper, classification models are used as tools to make final decision. Fuzzy method provides the mathematical tools for quantitative analysis and dealing with ambiguous concepts. Analytic Hierarchy Process (AH...In this paper, classification models are used as tools to make final decision. Fuzzy method provides the mathematical tools for quantitative analysis and dealing with ambiguous concepts. Analytic Hierarchy Process (AHP) is used to obtain the weight of each index and enables examiners to visualize the decision process and obtain more reasonable evaluation values to solve some problems. An example is given at the end of this paper.展开更多
A land use- and geographical information system-based framework was presented for potential human health risk analysis using soil sampling data obtained in Zhuzhou City, Hunan Province, China. The results show that he...A land use- and geographical information system-based framework was presented for potential human health risk analysis using soil sampling data obtained in Zhuzhou City, Hunan Province, China. The results show that heavy metal content in soil significantly differs among different land use types. In total, 8.3% of the study area has a hazard index(HI) above the threshold of 1.0. High HIs are recorded mainly for industrial areas. Arsenic((29)87%) and the soil ingestion pathway(about 76%) contribute most to the HI. The mean standardized error and root-mean-square standardized error data indicate that the land use-based simulation method provides more accurate estimates than the classic method, which applies only geostatistical analysis to entire study area and disregards land use information. The findings not only highlight the significance of industrial land use, arsenic and the soil ingestion exposure pathway, but also indicate that evaluating different land use-types can spatially identify areas of greater concern for human health and better identify health risks.展开更多
With the development of researches on the classification quality of remote sensing images, researchers thought that uncertainty is the main factor that influences classification quality. This study puts forward an app...With the development of researches on the classification quality of remote sensing images, researchers thought that uncertainty is the main factor that influences classification quality. This study puts forward an approach to uncertainty repre- sentation, which is developed from two aspects： formalized description and comprehensive evaluation. First, we complete the classification using fuzzy surveillance approach, taking it as a formalized description of classification uncertainty. Then we in- troduce a hybrid entropy model for classification uncertainty evaluation, which can meet the requirement of comprehensive reflection of several uncertainties, while constructing the evaluation index from pixel scale with the full consideration of the different contribution to the error rate of each pixel. Finally, an application example will be studied to examine the new method. The result shows that the evaluation results fully reflect the classification quality, when compared with the conventional evaluation method which constructs models from unitary uncertainty and category scale.展开更多
文摘In this paper, classification models are used as tools to make final decision. Fuzzy method provides the mathematical tools for quantitative analysis and dealing with ambiguous concepts. Analytic Hierarchy Process (AHP) is used to obtain the weight of each index and enables examiners to visualize the decision process and obtain more reasonable evaluation values to solve some problems. An example is given at the end of this paper.
基金Project(51204074)supported by the National Natural Science Foundation of ChinaProjects(201309051,PM-zx021-201212-003,PM-zx021-201106-031)supported by the National Environmental Protection Public Welfare Industry Targeted Research Fund,China
文摘A land use- and geographical information system-based framework was presented for potential human health risk analysis using soil sampling data obtained in Zhuzhou City, Hunan Province, China. The results show that heavy metal content in soil significantly differs among different land use types. In total, 8.3% of the study area has a hazard index(HI) above the threshold of 1.0. High HIs are recorded mainly for industrial areas. Arsenic((29)87%) and the soil ingestion pathway(about 76%) contribute most to the HI. The mean standardized error and root-mean-square standardized error data indicate that the land use-based simulation method provides more accurate estimates than the classic method, which applies only geostatistical analysis to entire study area and disregards land use information. The findings not only highlight the significance of industrial land use, arsenic and the soil ingestion exposure pathway, but also indicate that evaluating different land use-types can spatially identify areas of greater concern for human health and better identify health risks.
基金Supported by the Provincial Science Research Program in Hubei Province of China (No. ETZ2007A03)
文摘With the development of researches on the classification quality of remote sensing images, researchers thought that uncertainty is the main factor that influences classification quality. This study puts forward an approach to uncertainty repre- sentation, which is developed from two aspects： formalized description and comprehensive evaluation. First, we complete the classification using fuzzy surveillance approach, taking it as a formalized description of classification uncertainty. Then we in- troduce a hybrid entropy model for classification uncertainty evaluation, which can meet the requirement of comprehensive reflection of several uncertainties, while constructing the evaluation index from pixel scale with the full consideration of the different contribution to the error rate of each pixel. Finally, an application example will be studied to examine the new method. The result shows that the evaluation results fully reflect the classification quality, when compared with the conventional evaluation method which constructs models from unitary uncertainty and category scale.
