Abstract
The large-scale and super-strength development of mineral resources in mining concentrated area in long term has made great contributions to Chinese economic construction and development, but it has caused serious damage to the ecological environment even ecological imbalance at the same time. In this study, according to the characteristics of mining concentrated area, the scientific and practical ecological carrying capacity evaluation index system of mining concentrated area had been established. Taking Panxi mining concentrated area as an example, using remote sensing and GIS technology, applying remote sensing image and Statistical data, the ecological environment evaluation data of mining areas was extracted effectively. For the non-linear relationship between the evaluation indexes and the degree of ecological carrying capacity in study area, this study innovative took the evaluation of ecological carrying capacity of the study area by using artificial neural network whose training used SCE-UA algorithm that well overcome the slow learning and difficult convergence of traditional neural network algorithm. The results of ecological carrying capacity evaluation of the study area were objective, reasonable and the credibility was high. The results showed that the area distribution of five ecological carrying capacity grade types was basically normal, and the overall ecological environment situation of Panxi mining concentrated area was in the middle level, the degree of ecological carrying capacity in the north was higher than the south, and mining activities were dominant factors to cause eco-environmental damage and ecological carrying capacity. In this study, a comprehensive theory and technology system of regional ecological carrying capacity evaluation which included the establishment of ecological carrying capacity evaluation index system, processing of evaluation data and establishing of evaluation model. New ideas and methods had provided for ecological carrying capacity of mining concentrated area.
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