Temporal characteristics of ecological risk assessment indicators in coal-mining city with the application of LVQ method
Because the ability of selected indicators in assessing ecological risk at different temporal scales is not the same, it is necessary to clear the definite comparability of such indicators at temporal scale to explore a new method for dynamic assessing the ecological risk. In this case, five mining...
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| Veröffentlicht in: | Ying yong sheng tai xue bao 2015-03, Vol.26 (3), p.867-874 |
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| container_title | Ying yong sheng tai xue bao |
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| creator | Peng, Jian Tao, Jing-Xian Liu, Yan-xu |
| description | Because the ability of selected indicators in assessing ecological risk at different temporal scales is not the same, it is necessary to clear the definite comparability of such indicators at temporal scale to explore a new method for dynamic assessing the ecological risk. In this case, five mining cities in Liaoning Province were selected as the study area, with the application of learning vector quantization (LVQ) neural network, the significance of the indicators for the ecological risk assessment was quantitatively analyzed to clarify their characteristics at temporal scale. The expression with two-dimension (long-term and short-term) of temporal scale was put forward as a new method to assess the ecological risk for mining cities. The results showed that the amount of industrial SO2 removed per output value, the amount of industrial dust removed per output value, coverage rate of urban green space, precipitation, coordination degree among subsystems, percentage of mining practitioners, and current year i |
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In this case, five mining cities in Liaoning Province were selected as the study area, with the application of learning vector quantization (LVQ) neural network, the significance of the indicators for the ecological risk assessment was quantitatively analyzed to clarify their characteristics at temporal scale. The expression with two-dimension (long-term and short-term) of temporal scale was put forward as a new method to assess the ecological risk for mining cities. 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In this case, five mining cities in Liaoning Province were selected as the study area, with the application of learning vector quantization (LVQ) neural network, the significance of the indicators for the ecological risk assessment was quantitatively analyzed to clarify their characteristics at temporal scale. The expression with two-dimension (long-term and short-term) of temporal scale was put forward as a new method to assess the ecological risk for mining cities. The results showed that the amount of industrial SO2 removed per output value, the amount of industrial dust removed per output value, coverage rate of urban green space, precipitation, coordination degree among subsystems, percentage of mining practitioners, and current year i</abstract><cop>China</cop><pmid>26211071</pmid><tpages>8</tpages></addata></record> |
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| source | MEDLINE; EZB-FREE-00999 freely available EZB journals |
| subjects | China Cities Coal Mining Dust Ecology Neural Networks (Computer) Risk Assessment - methods Time Factors |
| title | Temporal characteristics of ecological risk assessment indicators in coal-mining city with the application of LVQ method |
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