**3. Results**

The ANN model was established using normalized rainfall and groundwater table data. The R<sup>2</sup> was selected for model performance evaluation. Thus, the sensitivity analysis was conducted to evaluate the importance of the input for the model simulation. All results are discussed below.

## *3.1. ANN Results*

Referring to the training, validation, and testing arrangements from Tayfur and Singh [48], Yang et al. [49] and Memarian et al. [45], respectively, this study used 70% of the data for training, 20% for validation, and 10% for testing. With respect to the PE and hidden layer selection, Cheung et al. sugges<sup>t</sup> 10 PEs in 1 hidden layer [50]. Thus, in this study, PEs were set at 10, and the hidden layer was set at 1. The simulated results revealed that the R<sup>2</sup> of the MLP model in Well 1 was 0.848, in Well 2 it was 0.854, in Well 3 it was 0.914, in Well 4 it was 0.897, in Well 5 it was 0.759, in Well 6 it was 0.841, and in Well 7 it was 0.812. All MLP model values showed high correlation (R<sup>2</sup> > 0.8) except for Well 5, which was the farthest well from the Lin-Bien River and the ARL. Comparing Well 5 with a similar location well (Well 6), the well screen of Well 6 (13–25 m, above sea level) was shallower than that of Well 5 (−17 to 5 m, above sea level). The MLP model performance values (R2) are shown in Table 3. The model testing correlation relationship of each well is presented in Figures 6–12.


**Table 3.** MLP model performance.

**Figure 6.** The model testing correlation relationship of Well 1.

**Figure 7.** The model testing correlation relationship of Well 2.

**Figure 8.** The model testing correlation relationship of Well 3.

**Figure 9.** The model testing correlation relationship of Well 4.

**Figure 10.** The model testing correlation relationship of Well 5.

**Figure 11.** The model testing correlation relationship of Well 6.

**Figure 12.** The model testing correlation relationship of Well 7.

#### *3.2. SA Results*

A sensitivity analysis was performed on the MLP model's simulation results to understand the relative sensitivity of each input to the output. All SA results are shown in Table 4 and Figure 13. Referring to Jha and Sahoo, the level of sensitivity was classified into five categories [20], as shown in Table 5. Based on the value of the sensitivity index for a particular input at the well, the influence level of the model's sensitivity to the input was ranked on a scale of 1 to 5 (Table 6). The sensitivity analysis indicated that, for past 5-day rainfall and rainfall intensity, R2 and RI3 were the highest and second highest sensitivities in Well 1; in Well 2, the results were R2 and R3; in Well 3, they were R4 and R2; in Well 4, they were RI2 and RI1; in Well 5, they were R5 and R4; in Well 6, they were RI4 and R2; and in Well 7, they were R4 and R5. The rainfall (R) is more sensitive than rainfall intensity (RI) in this research area. The highest and second highest sensitivities are listed in Table 7 (if the ranked result in the same value, the original SA index was compared).


**Table 4.** Sensitivity analysis index.

**Figure 13.** Sensitivity index of each input of the MLP model.

**Table 5.** Categorization of sensitivity and associated ranks.


**Table 6.** Ranked sensitivity analysis index of each input.



**Table 7.** Highest and 2nd highest sensitivities.

When considering the SA results and the information in the location map of the study area (Figure 2), one can see that Wells 1 to 3 are within the study catchment, which is adjacent to Lin-Bien River and the ARL, and responded faster in R1, R2, R3, RI1, RI2, RI3, respectively; Wells 4 to 7 are outside the catchment and far from the ARL; R4, R5, RI4, RI5 were found to be sensitive in Wells 5 to 7. It can be concluded that the groundwater table variation adjacent to the Lin-Bien River and the ARL is response-related with rainfall time-lag. It is worth noting that, although Well 4 is located outside the catchment, it still has a certain degree of sensitivity from past 3-day rainfall (R1, R2, R3, RI1, RI2, RI3, respectively), the reason for which is discussed below.

The sensitivity of each well is influenced by the well screen position. The reason for past 3-day rainfall (R1, R2, R3, RI1, RI2, RI3, respectively) being sensitive in Well 4 is that the well screen's location is similar to the one in Well 3. After considering the SA results about the information for each groundwater monitoring well (Table 2), it was observed that 2-day rainfall (R1, R2, RI1, RI2, respectively) was more sensitive due to the well screen location being above sea level, for example, in Wells 1, 2, and 6.
