## Appendix A List of Primary Studies (PS) in this Literature
[PS20] [PERSON], [PERSON], [PERSON], and [PERSON], \"Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.,\" _Water Resources Research_, vol. 53, no. 5. pp. 3878-3895, 2017. [PS21] [PERSON], [PERSON], and [PERSON], \"A Hybrid Methodology for Salinity Time Series Forecasting Based on Wavelet Transform and NARX Neural Networks,\" _Arab. J. Sci. Eng._, vol. 39, no. 10, pp. 6895-6905, 2014. [PS22] [PERSON] and [PERSON], \"A novel ensemble model of automatic multilayer perceptron, random forest, and zeroR for groundwater potential mapping,\" _Environ. Monit. Assess._, vol. 193, no. 11, 2021. [PS23] [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"Enhancing the understanding of hydrological responses induced by ecological water replenishment using improved machine learning models: A case study in Yongding River,\" _Sci. Total Environ._, vol. 768, p. 145489, 2021. [PS24] [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"Shallow aquifer monitoring using handpump vibration data,\" _J. Hydrol. X_, vol. 8, no. May, 2020. [PS25] [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"Comparison of physical and data-driven models to forecast groundwater level changes with the inclusion of GRACE--A case study over the state of Victoria, Australia,\" _J. Hydrol._, vol. 602, no. July, 2021. [PS26] [PERSON], [PERSON], and [PERSON], \"Simulation and prediction of supraper-mafrost groundwater level variation in response to climate change using a neural network model,\" _J. Hydrol._, vol. 529, pp. 1211-1220, 2015. [PS27] [PERSON], \"Climate Change, Land Use/Land Cover Change, and Population Growth as Drivers of Groundwater Depletion in Central Valleys, Oxacao, Mexico,\" 2019. [PS28] [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"Urmia lake water depth modeling using extreme learning machine-improved grey wolf optimizer hybrid algorithm,\" _Theor. Appl. Climatol._, vol. 146, no. 1-2, pp. 833-849, 2021. [PS29] [PERSON], \"On predictability of Groundwater Level in Shallow Wells Using Satellite Observations,\" pp. 507-514, 2018. [PS30] [PERSON] et al., \"Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management,\" _IEEE Access_, vol. 9, no. July, pp. 96092-96113, 2021. [PS31] [PERSON]. [PERSON], \"Spatial Downscaling of GRACE TWSA Data to Identify Spatiotemporal Groundwater Level Trends in the Upper Florida Aquifer, Georgia, USA,\" _2019_. [PS32] [PERSON], [PERSON], \"Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models,\" _Groundw. Sustain. Dev._, 2019. [PS33] [PERSON], [PERSON], [PERSON], and [PERSON], \"Ensemble modelling framework for groundwater level prediction in urban areas of India,\" _Sci. Total Environ._, vol. 712, p. 135539, 2020. [PS34] [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland,\" _J. Hydroinformatics_, vol. 20, no. 6, pp. 1227-1246, 2018. [PS35] [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], \"Analysis of the contribution rate of the influencing factors to land subsidence in the eastern beijing plain, China based on extremely randomized trees (ERT) method,\" _Remote Sens._, vol. 12, no. 18, pp. 1-21, 2020. [PS36] [PERSON]; [PERSON], \"Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine,\" 2019. [PS37] [PERSON] and [PERSON], \"Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity,\" _Cosci. Front._, vol. 12, no. 2, pp. 713-724, 2021. [PS38] [PERSON] et al., \"Comparative analysis of artificial intelligence models for accurate estimation of groundwater nitrate concentration,\" _Sensors (Switzerland)_, vol. 20, no. 20, pp. 1-23, 2020. [PS39] [PERSON]; [PERSON]; [PERSON], \"Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities,\" _J. Environ.