## 3 Results  
This study utilized the PCA to investigate the dominant environmental factors affecting land subsidence [37; 38; 39]. In this study, five factors were considered as the dominant environmental factors for land subsidence as listed in Table 3. According to Table 3, the following factors include factor #1 (percentage of agricultural land use), factor #2 (electricity consumption of wells), factor #3 (percentage of fine-grained soil), factor #4 (length of maximum drainage path), and factor #5 (accumulation depth of land subsidence from 2015 to 2020). Factor #1 was utilized by the survey results of the current state of land use from the NLSC, Ministry of the Interior. The proportion of the agricultural land use within 250 m of each MLCW was examined. Factor #2 was collected from the electricity consumption of the managed wells from the WRA. The electricity consumption of managed wells located at the depth of 0 to 60 m from 2015 to 2020 within a radius of 250 m centered on each MLCWwas then calculated. Factors, including factor #3 and factor #4, were generated from the borehole data from the CGS and WRA of Taiwan. Factor #5 was collected from the WRA. The PCA was used to analyze the environmental factors for land subsidence [37; 38; 39]. This study used the drilling data of 24 stratified subsidence monitoring wells (from the WRA) in Yunlin County, and the drilling data of 51 hydrogeological drilling wells (from the CGS) in Yunlin County. A total of 75 drilling wells were analyzed, where the average depth of the drilling wells was 200.75 m. This study calculated the environmental factors of each drilling well in the Yunlin County, and conducted a PCA to capture the relationship between each environmental factor and the land subsidence. To examine the influence of different environmental factors on land subsidence, the correlation coefficient was first calculated. The results of the correlation coefficient matrix were then obtained to evaluate the correlation between the factors. Table 4 depicts the results of the correlation coefficient matrix. According to Table 4, factor #5 (accumulation depth of land subsidence from 2015 to 2020) is the cumulative subsidence factor, which is positively correlated with the factor #1 (percentage of agricultural land use), factor #2 (electricity consumption of wells), and factor #3 (percentage of fine-grained soil). The results obtained show that the correlation with factor #2 (electricity consumption of wells) is the highest, which is 0.46, indicating the high electricity consumption of the wells increase the accumulated land subsidence. Since the main diagonal is the correlation coefficient of the factor itself, the correlation coefficients were all 1, as listed in Table 4.  
Table 5 shows the values of component loading in the PCA component matrix. According to Table 5, the loading of the factor in different principal components can be obtained to evaluate the correlation between the factor and the principal component (PC). Table 6 shows the eigenvalue and contribution table of the principal components. Each principal component has a certain degree of representativeness for the interpretation of the entire data, which is determined by the contribution rate. As depicted in Table 6, the eigenvalues and contribution rates of factors demonstrate that the cumulative contribution of the first two principal components is 64.06%. It was found that the first two principal components can explain 64% of the data, which is representative to a certain extent. The contribution is 93.31%, which means that the first four principal components can explain almost all the data. According to the loading of the first two principal components in Table 6, the distribution of principal components can then be analyzed. Figure 5 displays the distribution of the principal component. As shown in Figure 5, all factors utilized are positive on principal component 1.