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
A novel dynamic batch selective sampling algorithm based on version space analysis is presented. In the traditional batch selective sampling, example selection is entirely determined by the existing unreliable classif...A novel dynamic batch selective sampling algorithm based on version space analysis is presented. In the traditional batch selective sampling, example selection is entirely determined by the existing unreliable classification boundary; meanwhile, within a batch, examples labeled previously fail to provide instructive information for the selection of the rest. As a result, using the examples selected in batch mode for model refinement will jeopardize the classification performance. Based on the duality between feature space and parameter space under the SVM active learning fi：amework, dynamic batch selective sampling is proposed to address the problem. We select a batch of examples dynamically, using the examples labeled previously as guidance for further selection. In this way, the selection of feedback examples is determined by both the existing classification model and the examples labeled previously. Encouraging experimental results demonstrate the effectiveness of the proposed algorithm.展开更多
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.
文摘A novel dynamic batch selective sampling algorithm based on version space analysis is presented. In the traditional batch selective sampling, example selection is entirely determined by the existing unreliable classification boundary; meanwhile, within a batch, examples labeled previously fail to provide instructive information for the selection of the rest. As a result, using the examples selected in batch mode for model refinement will jeopardize the classification performance. Based on the duality between feature space and parameter space under the SVM active learning fi：amework, dynamic batch selective sampling is proposed to address the problem. We select a batch of examples dynamically, using the examples labeled previously as guidance for further selection. In this way, the selection of feedback examples is determined by both the existing classification model and the examples labeled previously. Encouraging experimental results demonstrate the effectiveness of the proposed algorithm.
