## Materials and Methods
### Machine Learning Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multicolumn{3}{c}{**Spray Height**} \\ \cline{2-5}  & **Nozzle Spacing** & **1 m** & **1.5 m** & **2 m** \\ \hline A & \(\backslash\) & 158.21 \(\pm\) 5.30 \(\mu\)m\({}^{\text{c}}\) & 162.58 \(\pm\) 5.24 \(\mu\)m\({}^{\text{b}}\) & 171.48 \(\pm\) 5.65 \(\mu\)m\({}^{\text{a}}\) \\ \hline  & 0.5 m & 161.15 \(\pm\) 4.96 \(\mu\)m\({}^{\text{d}}\) & 171.33 \(\pm\) 6.85 \(\mu\)m\({}^{\text{c}}\) & 173.12 \(\pm\) 7.02 \(\mu\)m\({}^{\text{b}}\) \\ B & 0.6 m & 163.03 \(\pm\) 4.92 \(\mu\)m\({}^{\text{d}}\) & 172.98 \(\pm\) 3.97 \(\mu\)m\({}^{\text{b}}\) & 178.55 \(\pm\) 2.97 \(\mu\)m\({}^{\text{a}}\) \\  & 0.7 m & 167.87 \(\pm\) 4.00 \(\mu\)m\({}^{\text{c}}\) & 168.71 \(\pm\) 3.96 \(\mu\)m\({}^{\text{c}}\) & 178.48 \(\pm\) 3.99 \(\mu\)m\({}^{\text{a}}\) \\ \hline  & 0.5 m & 2.8265 \(\pm\) 0.2591 mL/cm\({}^{2.4}\) & 1.9069 \(\pm\) 0.1819 mL/cm\({}^{2.4}\) & 1.6924 \(\pm\) 0.2573 mL/cm\({}^{2.4}\) \\ C & 0.6 m & 2.6962 \(\pm\) 0.3879 mL/cm\({}^{2.4}\) & 1.8879 \(\pm\) 0.2492 mL/cm\({}^{2.4}\) & 1.5997 \(\pm\) 0.2267 mL/cm\({}^{2.4}\) \\  & 0.7 m & 2.1961 \(\pm\) 0.2630 mL/cm\({}^{2.3}\) & 1.6391 \(\pm\) 0.1728 mL/cm\({}^{2.4}\) & 1.5222 \(\pm\) 0.1721 mL/cm\({}^{2.4}\) \\ \hline  & 0.5 m & 161.15 \(\pm\) 4.96 \(\mu\)m\({}^{\text{d}}\) & 171.33 \(\pm\) 6.85 \(\mu\)m\({}^{\text{c}}\) & 172.17 \(\pm\) 6.40 \(\mu\)m\({}^{\text{b}}\) \\ D & 0.6 m & 161.72 \(\pm\) 4.76 \(\mu\)m\({}^{\text{d}}\) & 172.98 \(\pm\) 3.97 \(\mu\)m\({}^{\text{b}}\) & 178.56 \(\pm\) 2.97 \(\mu\)m\({}^{\text{a}}\) \\  & 0.7 m & 165.59 \(\pm\) 3.91 \(\mu\)m\({}^{\text{d}}\) & 167.72 \(\pm\) 3.63 \(\mu\)m\({}^{\text{c}}\) & 178.37 \(\pm\) 3.46 \(\mu\)m\({}^{\text{a}}\) \\ \hline \hline \end{tabular} The data type is mean values \(\pm\) standard deviation; the same letter note indicates no significant difference at the \(p=0.05\) significant level, different letters indicate significant differences at \(p=0.05\) significant level.  
\end{table} 
Table 2: The mean values, standard deviation, and significant difference for all groups under different experimental conditions. most gradient-based learning methods and simpler than neural networks learning algorithms in most cases [41]. ELM was extended from the SLFNs (single-hidden layer feedforward neural networks) with additive or RBF hidden nodes to SLFNs with a wide variety of hidden nodes [42]. It includes an input layer, hidden layer, and output layer, input weights link the input layer to the hidden layer, and output weights link the hidden layer to the output layer. Input weights and hidden biases are randomly chosen, Moore-Penrose generalized inverse is used to determine the output weights [43]. ELM shows faster learning speed than SVM and tends to be more suitable in applications request speed and capability, it also can be applied to nonlinear modeling with its strong nonlinear learning ability. Radial basis function neural network (RBFNN) is a three-layer feedforward neural network that includes an input layer, a hidden layer and an output layer, taking radial basis function (RBF) as the kernel function. Because of its fast training speed and strong generalization ability, it has been widely used in discrimination and regression analysis [44]. The input layer connected with the external environment and consists of several perception neurons. Due to the output characteristics, there is only one hidden layer in the RBFNN, the hidden layer comprises some hidden nodes. The output layer comprises of some output nodes and acts as a responder for the input layer [45]. RBFNN creates complex decision regions by utilizing overlapping localized regions comprise of simple kernel functions [46]. There are two steps to accomplish the learning procedure of RBFNN, training of the kernel functions centers by using a clustering procedure, and calculating output weights by solving a system of linear equations [47]. The RBF kernels used in this study directly rely on the computation of the relevant distances and reduce the complexity of training.