Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2.5 forecasting
學年 108
學期 2
出版（發表）日期 2020-07-10
作品名稱 Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2.5 forecasting
作品名稱（其他語言）
著者 Pu-Yun Kow; Yi-Shin Wang; Yanlai Zhou; I-Feng Kao; Maikel Issermann; Li-Chiu Chang; Fi-John Chang
單位
出版者
著錄名稱、卷期、頁數 Journal of Cleaner Production 261, 121285
摘要 The fine particulate matter (e.g. PM2.5) gains an increasing concern of human health deterioration. Modelling PM2.5 concentrations remains a substantial challenge due to the limited understanding of the dynamic processes as well as uncertainties residing in the emission data and their projections. This study proposed a hybrid model (CNN-BP) engaging a Convolutional Neural Network (CNN) and a Back Propagation Neural Network (BPNN) to make accurate PM2.5 forecasts for multiple stations at multiple horizons at the same time. The hourly datasets of six air quality and two meteorological factors collected from 73 air quality monitoring stations in Taiwan during 2017 formed the case study. A total of 639,480 hourly datasets were collected and allocated into training (409,238, 64%), validation (102,346, 16%), and testing (127,896, 20%) stages. The forecasts of PM2.5 concentrations were first characterized as a function of air quality and meteorological variables. Then the proposed CNN-BP approach effectively learned the dominant features of input data and simultaneously produced accurate regional multi-step-ahead PM2.5 forecasts (73 stations; t+1−t+10). The results demonstrate that the proposed CNN-BP model is remarkably superior to the BPNN, the random forest and the long short term memory neural network models owing to its higher forecast accuracy and excellence in creating reliable regional multi-step-ahead PM2.5 forecasts. Besides, the CNN-BP model not only has the power to cope with the curse of dimensionality by adequately handling heterogeneous inputs with relatively large time-lags but also has the capability to explore different PM2.5 mechanisms (local emission and transboundary transmission) for the five regions (R1-R5) and the whole Taiwan. This study shows that multi-site (regional) and multi-horizon forecasting can be achieved by exactly one model (i.e. the proposed CNN-BP model), hitting a new milestone. Therefore, the CNN-BP model can facilitate real-time PM2.5 forecast service and the forecasts can be made publicly available online.
關鍵字
語言 en
ISSN 0959-6526
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 NLD
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/124044 )