人工神经网络
空气质量指数
计算机科学
均方误差
平均绝对百分比误差
人工智能
波动性(金融)
时间序列
小波变换
机器学习
深度学习
小波
数据挖掘
计量经济学
气象学
统计
数学
地理
作者
Yongkang Zeng,Jingjing Chen,Ning Jin,Xiaoping Jin,Yang Du
标识
DOI:10.1016/j.buildenv.2022.108822
摘要
Air quality measurements and forecasting is one of the most popular research topics in the field of sustainable intelligent environmental design, urban area development and pollution control, especially for Asia developing countries, such as China. Deep learning (DL) technologies for time series data forecasting, such as the recurrent neural network (RNN) and long short term memory (LSTM) neural network, have attracted extensive attentions in recent years and have been applied to AQI forecasting. However, two problems exist in the literature. First, the volatility of the AQI data causes difficulties for singular DL models to produce reliable forecasting results. Second, a long history of the air-quality data is required in the training stage, which is usually unavailable. A novel forecasting model that integrates the extended stationary wavelet transform (ESWT) and the nested long short-term memory (NLSTM) neural network for PM2.5 air quality forecasting is proposed in this study. The results show that the proposed method outperforms state-of-art forecasting methods and recently published works in terms of different error metrics, such as absolute error, R2, MAE, RMSE, and MAPE.
科研通智能强力驱动
Strongly Powered by AbleSci AI