| Early-warning of ecological safety based on BP artificial neural network——A case study of Gansu Province |
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| DOI：10.7606/j.issn.1000-7601.2012.01.36 |
| Key Words: BP artificial neural network ecological safety early-warning model Gansu Province |
| Author Name | Affiliation | | GONG Jiping | College of Geography and Environmental Science, Northwest Normal University, Lanzhou, Gansu 730070, China | | SHI Peiji | College of Geography and Environmental Science, Northwest Normal University, Lanzhou, Gansu 730070, China | | WEI wei | College of Geography and Environmental Science, Northwest Normal University, Lanzhou, Gansu 730070, China |
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| Abstract: |
| Combining with the theory of early-warning, the “State-Danger-Immune” (SDI) model was used as a concept model of early-warning indicators. As a case study, BP artificial neural network model was employed to assess quantitatively the ecological safety situation of Gansu Province from 1997—2009. Then the early-warning was carried out with the model for ecological safety from 2010—2015. The results showed that: (1) It was feasible to do early-warning study on ecological safety by using BP artificial neural network model because of its high precision of simulation result; (2) The ecological environment shifted from “relatively safe” to “relatively unsafe” state from 1997—2009, which demonstrated that the overall status of ecological safety in the province undergwent a decreasing trend. (3) Under the early-warning results, the ecological environment in Gansu from 2010—2015 is getting worse, and the status of resources safety and ecological safety is not optimistic. The industrial waste gas emissions per capita, energy consumption per capita, rate of comprehensive utilization of industrial solid waste, insufficient investment in environmental pollution control will be the main factors of ecological safety in the province. |
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