计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 184-189.doi: 10.11896/jsjkx.200600090
刘梦炀1,2, 武利娟1,3, 梁慧1,3, 段旭磊1,3, 刘尚卿1,3, 高一波1,3
LIU Meng-yang1,2, WU Li-juan1,3, LIANG Hui1,3, DUAN Xu-lei1,3, LIU Shang-qing1,3, GAO Yi-bo1,3
摘要: 大气污染已经严重影响到人们的生活和健康,大气治理势在必行,探究大气污染物浓度变化的规律,实现污染物浓度预测,对指导大气治理工作具有重要意义。文中构建了一种基于长短期记忆神经网络(Long Short-Term Memory,LSTM)和全连接神经网络(Full Connected,FC)的混合神经网络模型,并提出了数据桶划分的训练方式来解决由于训练数据与预测数据存在较长时间间隔导致精度下降的问题,进而实现大气污染物浓度的预测。该模型具有较好的通用性和精度,充分结合了长短期记忆神经网络和全连接神经网络的优点,能够在多种污染物数据上实现精确预测。以天津市2013-2019年大气污染物数据实现模型的训练和预测,结果表明,混合神经网络模型在PM2.5,PM10,NO2,SO2,O3,CO 6种污染物浓度的预测上均可以达到R2>0.90,平均百分误差小于15%的效果,LSTM-FC模型在大气污染物预测中具有明显的优势,具有较高的实用价值。
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