空气质量预报二次建模

Secondary Modeling of Air Quality Forecasting

建立空气质量预报模型提前获知可能发生的大气污染状况,并采取相应控制措施是减少大气污染危害的有效方法之一。本文针对WRF-CMAQ等模型预报结果不理想的问题,提出了基于一次预报数据和实测数据的二次预报模型,提高了一次预报模型的准确性,分析了气象条件对污染物浓度的影响程度,提出了时间序列预测模型和基于粒子群优化的BP神经网络模型,大大地提高了预测的精度。

One of the effective ways to reduce the harm of air pollution is to establish an air quality forecast model to know the possible air pollution situation in advance and take corresponding control measures. Aiming at the problem that WRF-CMAQ and other models have unsatisfactory forecast results, this paper puts forward a secondary forecast model based on primary forecast data and measured data, which improves the accuracy of the primary forecast model, analyzes the influence of meteorological conditions on pollutant concentration, and puts forward a time series forecast model and a BP neural network model based on particle swarm optimization, which greatly improves the forecast accuracy.