A data-driven intelligent decision of the emission reduction scheme for heavy air pollution emergency response
Production reduction of industrial polluters is one of the most effective measures for heavy air pollution emergency response.Current response methods fail to consider the diffusion conditions and the contradiction between atmospheric regulation and economic development.Addressing those challenges,a data-driven intelligent decision(DID)method is developed.Orienting to diffusion conditions,an extreme learning machine method is applied to forecasting the emission reduction effect of alternative plans,which are then optimized under the two constraints of improving air quality and economic output.Experiments on real datasets demonstrate that the DID schemas are superior to current plans in terms of improving air quality and economic benefit.The DID can give more pertinent and scientific heavy pollution emergency response plans.Managers can set corresponding targets in the DID to balance atmosphere environment protection and economic development.As an effective and low-cost schema,the DID is rational and feasible to be put into practice.
万方数据
维普
