## 1 Introduction  
Due to overexploitation of groundwater resources, there is a worldwide land subsidence problem [1, 2]. Recent climate anomalies have continued to disturb the equilibrium of water resources and to increase the frequency of flooding and droughts [3, 4, 5]. The consumption of water demand from the use of groundwater has continued to accelerate in the Choshui delta of central Taiwan [6, 7, 8]. The overexploitation of groundwater resources has led to the compaction of the aquifer systems, which may cause rapid land subsidence, and has taken place in countries such as Italy, United States, Mexico, Japan and Taiwan [9, 10, 11, 12, 13]. By the end of 2021, the maximum subsidence rate reached 7.8 cm/year in the Choshui delta of central Taiwan.  
Previous research has summarized that the key to the land subsidence problem was the correct assessment of the aquitards' behavior [14, 15, 16]. Consequently, the deformation of aquiters was frequently underestimated. According to the results of the predictive model, the behavior of sandy and clay layers obeys the elastic phenomena and the Terzaghi consolidation theory, respectively [17, 18]. Land subsidence is strongly affected by seasonal variations with precipitation and declining groundwater levels [19]. The effect of groundwater level variations on land subsidence can be simulated based on a stress-strain relationship [20]. The modular three-dimensional groundwater flow mode (MODFLOW) model of groundwater flow and the IBS model of land subsidence were utilized to evaluate the stratum compaction coefficients and the groundwater safe yield [21]. The MODFLOW-2000, subsidence and aquifer-system compaction package (SUB) and a computer code for universal inverse modeling (UCODE) have also been used to model groundwater flow and land subsidence, and estimate the compaction coefficients in Antelope Valley, California [22]. Several artificial intelligence technologies have been applied for the analysis of land subsidence susceptibility. A new fuzzy-catastrophe scheme has been proposed to transform subsidence vulnerability indexing based on ALPRIFT into risk indexing [23]. To estimate the possible land subsidence time variations, the dynamic subsidence vulnerability index was developed by considering changes in groundwater level based on the ALPRIFT framework [24]. A two-level modeling strategy by combining multi-criteria decision-making and artificial intelligence techniques was conducted to map land subsidence risk [25]. Despite the success of the above artificial intelligence techniques as effective numerical tools for subsidence assessment, there is still growing interest in the development of a robust predictive model using artificial intelligence technologies.  
To characterize the spatial variability in land subsidence and its relation to groundwater withdrawals in the Choshui delta, this study collects several data including in situ monitoring data, such as levelling surveys, borehole logging, multi-layer compaction monitoring network, multi-layer groundwater level monitoring network, and the electricity consumption of wells in the study area. Various factors, including the percentage of coarse-grained soil, percentage of fine-grained soil, length of average maximum drainage path, percentage of agricultural land use, electricity consumption of wells, and accumulated depth of land subsidence, are applied. Thematic maps based on these factors are established using geographic information system spatial analysis. A principal component analysis (PCA) is then employed to obtain the dominant factors for the land subsidence. This work is organized as follows. The materials and methods are introduced in Section 2. In Section 3, the results are analyzed. The discussion is conducted in Section 4, and the significant findings of this research are summarized in this section as well. Conclusions are presented in Section 5.