As the largest hydropower project in China, the impact of the Three Gorges Project (TGP) on the ecological environment has aroused public attention and has been studied from various aspects. After the TGP was operated, seasonably adjusted runof f by the Three Gorge Reservoir (TGR) might cause great changes of hydrological regime in the lower reaches of the Yangtze River, especially the estuarine areas

Ambient air PM2.5 is one of the major pollutants linked to respiratory and lung diseases in the Yangtze River Delta (YRD), which is China’s leading economic region and one of the top economic regions worldwide. The main objectives of this work is to compare the accuracy of some widely-used techniques to characterize and predict the space-time distribution of ground-level PM2.5 in the YRD, and to propose

Resilience is a multifaceted concept describing the ability to cope with change or disruption. Its importance in the era of emergency preparedness and response, combined with its multidisciplinary attributes, have led researches to study similarities and differences in the meaning of resilience across various fields. A systematic literature review, conducted in the field of resilience management by

Information provision is a relatively recent but steadily growing environmental policy tool. Its emergency and topicality are due to the current escalation of ecological threats. Meanwhile, its high complexity and flexibility require a comprehensive approach to its design, which has to be tailored for specific characteristics of production process, market structure, and regulatory goals. This work

Recently the issue of air quality has become a global public health concern. As air pollution has been reported as the largest single environmental health risk in the world, analysis and prediction of air quality is increasingly important. Normally, either statistical models or CTM (“deterministic chemistry-transport”) models are used for forecasting air PM (“particulate matter”) levels. Actually,

This article proposes and verifies a novel intelligence approach for modelling forest fire danger, namely ICA-RVM, developed based on Relevance Vector Machine (RVM) and Imperialist Competitive Algorithm (ICA), state-of-the art machine learning techniques that have not been investigated for forest fire danger modeling. RVM is used to establish a prediction model that computes probability of fire danger