University of Belgrade, Innovation Center of the Faculty of Technology and Metallurgy, Karnegijeva 4, 11120 Belgrade, Serbia.
Sci Total Environ. 2013 Jan 15;443:511-9. doi: 10.1016/j.scitotenv.2012.10.110. Epub 2012 Dec 4.
This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM(10) emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM(10) emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM(10) emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM(10) emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables.
本文描述了一种人工神经网络（ANN）模型的开发，用于预测国家层面的年度 PM(10)排放量，该模型使用广泛可用的可持续性和经济/工业参数作为输入。使用遗传算法选择和优化模型的输入，使用以下变量对 ANN 进行训练：国内生产总值、内陆能源总消耗、木材焚烧、机动车化率、纸和纸板产量、锯材产量、精炼铜产量、原铝产量、生铁产量和粗钢产量。该模型使用的输入参数广泛可用，可以克服许多国家数据和基本环境指标的缺乏，这些缺乏可能会阻碍或严重阻碍 PM 排放预测。该模型使用 1999 年至 2006 年期间 26 个欧盟国家的数据进行了训练和验证。PM(10)排放数据通过《远距离越境空气污染公约》- CLRTAP 和欧洲环境署方案收集，或通过区域空气污染信息和模拟（RAINS）模型作为排放估算获得，这些数据来自欧盟统计局。ANN 模型表现出非常好的性能，表明可以成功且准确地预测长达两年的 PM(10)排放。两年期 PM(10)排放预测的平均绝对误差仅为 10%，比使用相同数据集和输入变量进行训练和测试的传统多元线性回归和主成分回归模型的预测结果要好 3 倍以上。
