【摘要】 生态环境是和社会经济紧密联系、相互影响的复杂大系统 ,且其内部各因子之间存在大量的未知关系 ,神经网络为未知复杂系统的建模提供了一条全新的思路。运用 BP网络 ,就南水北调对湖北省襄樊市生态服务功能价值的影响进行了动态研究。研究显示 ,在调走约 1/3汉江水量后 ,襄樊的生态环境将受到严重的破坏 ,其后果是当地社会经济的可持续发展能力将受到损害和抑制。在分析案例的基础上 ,论证了神经网络在生态建模中的适用性 ,探讨了生态服务功能价值动态评价的技术趋势 ,并进一步提出了生态系统建模的新思路。更多还原

【Abstract】 The services of ecological environment are critical to socio-economy by underpinning human welfare.Currently, how to dynamically predict ecosystem service value (ESV) is a multidisciplinary sharp-edge issue in the research field of sustainable development. Artificial neural networks (ANNs) has been proved to perform better than many classical modeling methods in an increasing number of applications in ecological modeling.However, too few applications have been reported in ecological economics. In this paper, by a case study of dynamic assessing ESV of Xiangfan, Hubei Province after south to north water transfer project in China, a BP network was built to simulate ESV loss of that district brought by the huge man-made project.Since the excellent nonlinear approximation ability of a BP network is based on properly determining the topology and structural parameters, learning efficient training sets with good typical characters and searching the global minimum solutions, the authors adopted some methods in this paper to avoid “multimodal”, “overfitting” and “overlearn” which always occur in BP modeling process.As ecological environment is closely connected with and interacts with socio-economy, they should be looked at as a whole.In order to grasp the inherent essential relationship between them,the authors put both ecological environmental factors and socio-economic factors together to set up a 3-layer BP network in this paper. Historical data of ecological environmental factors and socio-economic factors were used as inputs and corresponding historical data of ESV and GDP (gross domestic product) as target outputs to train the network.The network was concluded to have been trained when the test errors were controlled within 1.4% after iteratively calculating outputs and adjusting weights and biases. So it could be used for generalization. Then predicted data of ecological environmental factors and socio-economic factors after 2001 were presented to the trained network as generalization sets, ESVs and GDPs of 2002, 2003, 2004. till 2050 were simulated as output in succession. According to simulation results, up to 2050, the district would have suffered an accumulative total ESV loss of RMB104.9 billion, which accounts for 37.36% of the present ESV. While coinstantaneous GDPs would change asynchronously with ESVs, they would go through an up-to-down process and finally lose RMB89.3 billion, which accounts for 18.71% of 2001. The simulation indicated that ESV loss means damage to the capability of socio-economic sustainable development.To avoid loss of socio-economy, the authors put forward a series of countermeasures and retrieving plan to uphold the regional ecosystem service. On the base of case study, the authors also dealt with the applicability of ANNs in ecosystem modelling. Although a good few other published studies’ results showed ANNs perform better than other modeling methods,many of them were just restricted within simple narrating .This paper further proved that in theory. Besides, the authors pointed out it seems more harmonious with the nature to use ANNs in ecosystem modeling. In the end, the authors reached conclusions: first, this huge man-made project would weaken the ecosystem service value (ESV) of that district and hold back the development of the local economy to a large extent, which manifests it is by providing the ability of sustainable development that ecosystem serves socio-economy. Second, the authors demonstrated in theory that ANNs have perfect applicability in ecosystem modelling. Meanwhile the authors indicated impersonally that any single approach couldn’t meet all the needs of a complex incomprehension system modeling. So they brought forward suggestions for ecosystem modeling: first of all, to grasp the integration and dynamic by function simulating; then, to further research the inner topology and processes of the system under the guidance of the integration and dynamic by structure simulating, finally to harmonize the simulated topology and functions of the ecosystem.更多还原