## 3 Methods
### Major Artificial Intelligence Models
MARS is a non-parametric regression technique that combines a series of linear models to cope with nonlinearity and interactions between variables. The learning dataset is divided into adaptive splines with different slopes. In general, the splines are smoothly connected to each other, and the polynomial function for the splines can generate a flexible model that can handle nonlinear relationships between data [42; 43]. The suitability of a MARS model can be evaluated using the generalized cross-validation (GCV) value [44]. SVM is a machine-learning algorithm that performs optimal grouping of data and builds a regression model for the optimally divided dataset [45; 46; 47]. A hyperplane is used for the optimal grouping of data [48; 49; 50] by maximizing the margin between groups. The MMH can be found by maximizing the margin between support vectors at the boundary of the data groups [51], using kernel functions such as linear and Gaussian radial basis functions (RBFs). RF, which represents an improved version of classification and regression trees (CART), is an ensemble model using the bootstrap and bagging technique [52]. It generates a large number of decision trees with slightly different features by extracting random samples from the training data, including a bootstrap that determines the suitability of the sampling distribution, and conducts resampling as needed. During the bagging process, bootstrap-based decision trees are aggregated to create a final solution, using an ensemble model such as the average or majority vote [13; 53]. The final decision tree can be optimized by the tree pruning algorithm to determine an optimal size for the trees. In our experiment, the number of trees was set to 500, and the number of variables used for splitting nodes was set to n/3 (n = number of input variables). In addition, the out-of-bag error was used as the criterion of model suitability. ERT is an ensemble model similar to RF, but it uses unpruned decision trees. ERT divides the nodes by randomly chosen cut-points and incorporates the complete learning sample (without bootstrap copying) to grow the trees [54]. The two main parameters for ERT are the number of attributes randomly selected at each node and the minimum sample size for splitting the node. The predictions of the trees are aggregated to produce a final ensemble prediction. In our experiment, the number of trees and the number of variables used for splitting nodes were set to be the same as those of RF. ANN is a network model for emulating a biological neural system, and consists of input, hidden, and output layers [55]. The input layer corresponds to the explanatory variables, and the hidden layer represents the core of the node-link network for the computation of nonlinear problems [56; 57]. Factors affecting the performance of ANNs include the number of nodes in the hidden layer, the learning rate, and the training tolerance [55]. The learning rate determines the amount by which the weights change during a series of iterations to bring the predicted value within an acceptable range of the observed value; furthermore, the training tolerance refers to the maximum error rate at which the network must converge during training [58]. Through this process, we can optimize a weight and bias set for the node-link structure and produce an approximation of the answer. In our experiment, the number of neurons in the hidden layer was set to three, which was selected through a performance test. The classic ANN has a local minima problem in which an optimization process often stops at a locally, rather than globally, optimized state. In addition, generic machine learning models sometimes have problems with overfitting, in which they cannot handle data with outliers due to excessive learning from the given dataset. Such problems can be resolved by DNNs through an intensive optimization process in a deep network structure. To handle local minima and issues with overfitting, L1/L2 regularization can be employed to ensure sparsity (L1) and simplicity (L2) of the DNN model.