Abstract
Gradually deteriorating eco-systems have been threatening the sustainable development of cities. With time, more negative impacts will emerge and affect national security. Cities face significant challenges in ecological forward with sustainable development. Yangtze River Delta is recognized as one of the sixth largest urban agglomeration in the world, its economic development plays a significant role in the country and its ecological security cannot be ignored. In order to model and evaluate the urban ecological security of the Yangtze River Delta, Machine Learning method was integrated with the Pressure-State-Response (PSR) model. On comparison with the traditional methods, the process for modeling urban ecological security is more efficient, reliable, comprehensive and objective. The results show that (1) The ecological security of the Yangtze River Delta has been generally improved from the past 10 years; (2) Industrial smoke (powder) dust emissions, per capita park green area, and science technology expenditures in fiscal expenditure are the three most influencing indicators with aspect to nature, society, and economy. This research could provide guidance to the construction and maintenance of urban ecological security.
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