Data modeling
Because of the complexity of the anthropogenic driving mechanisms, the
relations among the 23 independent variables and ESV cannot be described
by the conventional linear model. We used a
feed-forward dense network as the deep learning model.
The network consists of 8 layers (4 dense layers), including 6 hidden
layers in the network. Each layer has a certain number of neurons and
activation functions (Table 1). Nonlinear activation functions such as
the rectified linear unit (ReLU) were introduced in the
3rd and 5th hidden layers to learn
the nonlinearity. The ReLU function was used to avoid vanishing gradient
problems. Additionally, the dropout rate was set to 0.3 in all dropout
layers to avoid overfitting problems.
We partitioned 70% of the 2191 units as training samples and 30% as
testing samples. In the training phase, the optimizer and loss function
were established based on adaptive moment estimation (ADAM) and the mean
square error (MSE). After conventional model optimizations were
performed, the above hyperparameters were determined. The corresponding
model was trained and used in the study.
Table 1. The configuration of the model