Using support vector machine to predict eco-environment burden: a case study of Wuhan, Hubei Province, China / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>The human socio-economic development depends on the planet's natural capital. Humans have had a considerable impact on the earth, such as resources depression and environment deterioration. The objective of this study was to assess the impact of socio-economic development on the ecological environment of Wuhan, Hubei Province, China, during the general planning period 2006-2020.</p><p><b>METHODS</b>Support vector machine (SVM) model was constructed to simulate the process of eco-economic system of Wuhan. Socio-economic factors of urban total ecological footprint (TEF) were selected by partial least squares (PLS) and leave-one-out cross validation (LOOCV). Historical data of socio-economic factors as inputs, and corresponding historical data of TEF as target outputs, were presented to identify and validate the SVM model. When predicted input data after 2005 were presented to trained model as generalization sets, TEFs of 2005, 2006,..., till 2020 were simulated as output in succession.</p><p><b>RESULTS</b>Up to 2020, the district would have suffered an accumulative TEF of 28.374 million gha, which was over 1.5 times that of 2004 and nearly 3 times that of 1988. The per capita EF would be up to 3.019 gha in 2020.</p><p><b>CONCLUSIONS</b>The simulation indicated that although the increase rate of GDP would be restricted in a lower level during the general planning period, urban ecological environment burden could not respond to the socio-economic circumstances promptly. SVM provides tools for dynamic assessment of regional eco-environment. However, there still exist limitations and disadvantages in the model. We believe that the next logical step in deriving better dynamic models of ecosystem is to integrate SVM and other algorithms or technologies.</p>

Forecasting loss of ecosystem service value using a BP network: a case study of the impact of the South-to-north Water Transfer Project on the ecological environmental in Xiangfan, Hubei Province, China / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>To recognize and assess the impact of the South-to-north Water Transfer Project (SNWTP) on the ecological environment of Xiangfan, Hubei Province, situated in the water-out area, and develop sound scientific countermeasures.</p><p><b>METHODS</b>A three-layer BP network was built to simulate topology and process of the eco-economy system of Xiangfan. Historical data of ecological environmental factors and socio-economic factors as inputs, and corresponding historical data of ecosystem service value (ESV) and GDP as target outputs, were presented to train and test the network. When predicted input data after 2001 were presented to trained network as generalization sets, ESVs and GDPs of 2002, 2003, 2004... till 2050 were simulated as output in succession.</p><p><b>RESULTS</b>Up to 2050, the area would have suffered an accumulative total ESV loss of RMB104.9 billion, which accounted for 37.36% of the present ESV. The coinstantaneous GDP would change asynchronously with ESV, it would go through an up-to-down process and finally lose RMB89.3 billion, which accounted for 18.71% of 2001.</p><p><b>CONCLUSIONS</b>The simulation indicates that ESV loss means damage to the capability of socio-economic sustainable development, and suggests that artificial neural networks (ANNs) provide a feasible and effective method and have an important potential in ESV modeling.</p>