  Densely connected convolutional networks (DenseNet) behave well in image
processing. However, for regression tasks, convolutional DenseNet may lose
essential information from independent input features. To tackle this issue, we
propose a novel DenseNet regression model where convolution and pooling layers
are replaced by fully connected layers and the original concatenation shortcuts
are maintained to reuse the feature. To investigate the effects of depth and
input dimension of proposed model, careful validations are performed by
extensive numerical simulation. The results give an optimal depth (19) and
recommend a limited input dimension (under 200). Furthermore, compared with the
baseline models including support vector regression, decision tree regression,
and residual regression, our proposed model with the optimal depth performs
best. Ultimately, DenseNet regression is applied to predict relative humidity,
and the outcome shows a high correlation (0.91) with observations, which
indicates that our model could advance environmental data analysis.
