| 英文摘要： |
For exposure estimation of air pollutants, data measurement errors and modeling uncertainty may lead to estimation bias and abnormal predictions and relationships between variables. This paper proposes a method of geospatial constrained optimization and deep learning to reliably simulate and predict spatiotemporal trends of air pollutants. In the proposed method, k-nearest neighbors (k-NN) was first used to retrieve the nearest samples to spatialize regular local temporal basis functions at each target location or subregion; then, a convolutional neural network (CNN) was used to extrapolate temporal basis functions for prediction. Domain and empirical knowledge was embedded in extensive constrained optimization to obtain reasonable simulations and predictions. Bootstrapping was used to estimate the uncertainty of constrained optimized values. The method reduced the bias in the point estimates and obtained robust predictions and their uncertainty estimates of spatiotemporal trend for each spatial target location. In the location-based validations of NO2, NOx and PM2.5 in California, even with limited noise input, the proposed method captured the primary spatiotemporal variability (correlation with measured values: 0.75-0.91; explaining 55-84% of the variance). In addition, compared with generalized additive spatiotemporal model, kernel smoother and CNN, the proposed method made one-year reliable spatiotemporal forecasts of weekly averages. The proposed method has important implications for reducing the estimation bias and predicting trends in the air pollutant spatiotemporal fields. (C) 2021 Elsevier B.V. All rights reserved. |
在线留言
联系我们
所长信箱