… emissions are from road transport ie vehicular emissions. To control vehicular emission,
first, an efficient emission … In this paper, a scalable vehicle CO2 emission prediction model is …

… The goal of this research is to achieve predictions with a much better accuracy. The … deep
learning CNN – LSTM hybrid neural network for predicting the Carbon-dioxide emissions in …

… Aiming at the shortcomings of existing methods, this study proposes an ensemble deep
learning model for exhaust emissions prediction. A stacked denoising autoencoder (SDAE) is …

… Our study is the first to apply deep learning technology in predicting the air quality response
to emission changes by linking CNN and RSM technologies using a carefully selected set of …

… emission prediction. In this research, a model combining CEEMDAN and LSTM for predicting
NOx emissions from … and non-linearity of real road emission data. The contributions are as …

… Recently, deep learning has played an important role in the … can be predicted with high
accuracy using deep learning because … with deep neural networks for gasoline engine prediction. …

… A key performance metric of a machine learning model is its prediction accuracy on unseen
… of the deep learning based emissions model was investigated by evaluating its prediction …

… for predicting pollutant concentrations, the emission inventory can be updated based on
the error between the observed and NN-CTM-predicted … grids' emission inventory within the …

… NO x emission prediction sequence of LSTM Figure 3 presents the NO x emission prediction
sequence of the LSTM model used in this study. The data contained multiple input features …

… emissions are declining more slowly after 2009. Here, we use a deep learning model trained
by NO x emissions … best reproduce ozone in low NO x emission (background) regions. The …