## Appendix A List of Primary Studies (PS) in this Literature  
Appendix A provides the list of primary studies (PS) used in this literature. [PS1] [PERSON] et al., \"Simulation of regional groundwater levels in arid regions using interpretable machine learning models,\" _Sci. Total Environ._, vol. 831, no. 2022, p. 154902, 2022. [PS2] [PERSON], \"Machine Learning Algorithms for soil\". pp. 1-17, 2017. [PS3] [PERSON], [PERSON], [PERSON], and [PERSON], \"Land subsidence prediction induced by multiple factors using machine learning method,\" _Remote Sens._, vol. 12, no. 24, pp. 1-17, 2020. [PS4] [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms,\" _Adv. Water Resour._, vol. 141, no. May, 2020. [PS5] [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"An autoregressive machine learning approach to forecast high-resolution groundwater-level anomalies in the Ramotswa/North West/Gauteng dolomite aquifers of Southern Africa,\" _Hydrogeol. J._, vol. 30, no. 2, pp. 575-600, 2022. [PS6] [PERSON], [PERSON], and [PERSON], \"Application of random forest and multi-linear regression methods in downscaling GRACE derived groundwater storage changes,\" _Hydrol. Sci. J._, vol. 66, no. 5, pp. 874-887, 2021. [PS7] [PERSON], [PERSON], [PERSON], and [PERSON], \"A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level,\" pp. 1-25, 2022. [PS8] [PERSON], \"Forecasting Groundwater Table in a Flood Prone Coastal City with Long Short-term Memory and Recurrent Neural Networks,\" 2019. [PS9] [PERSON], [PERSON], and [PERSON], \"Assessment and Prediction of Groundwater using Geospatial and ANN Modeling,\" _Water Resour. Manag._, vol. 35, no. 9, pp. 2879-2893, 2021. [PS10] [PERSON], [PERSON], [PERSON], and [PERSON], \"Potential impacts of climate change on groundwater level through hybrid soft-computing methods: a case study--Shabestar Plain, Iran,\" _Environ. Monit. Assess._, vol. 191, no. 10, 2019. [PS11] [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"Adaptive water management in the face of uncertainty: Integrating machine learning, groundwater modeling and robust decision making,\" _Clim. Risk Manag._, vol. 34, no. May, p. 100383, 2021. [PS12] [PERSON] et al., \"Improving the resolution of grace data for spatio-temporal groundwater storage assessment,\" _Remote Sens._, vol. 13, no. 17, 2021. [PS13] [PERSON] et al., \"Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR,\" _Sci. Total Environ._, vol. 799, p. 149244, 2021. [PS14] [PERSON], \"Downscaling GRACE TWSA data into high-resolution groundwater level anomaly using machine learning-based models in a glacial aquifer system,\" _MDPI_, 2019. [PS15] [PERSON], [PERSON], and [PERSON], \"Prediction of landslide sharp increase displacement by SVM with considering hysteresis of groundwater change,\" _Eng. Geol._, vol. 280, no. August 2020, p. 105876, 2021. [PS16] [PERSON], [PERSON], and [PERSON], \"Impact of projected climate change on seawater intrusion on a regional coastal aquifer,\" _J. Earth Syst. Sci._, vol. 129, no. 1, 2020. [PS17] [PERSON] et al., \"Can ensemble machine learning be used to predict the groundwater level dynamics of farmland under future climate: a 10-year study on Huaibei Plain,\" _Environ. Sci. Pollut. Res._, no. 0123456789, 2022. [PS18] [PERSON], [PERSON], and [PERSON], \"Deep learning shows declining groundwater levels in Germany until 2100 due to climate change,\" _Nat. Commun._, vol. 13, no. 1, pp. 1-13, 2022. [PS19] [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data,\" _J. Hydrol._, vol. 603, no. PA, p. 126929, 2021.