###### Abstract  
Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth's surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level prediction is a critical aspect of water resource management and requires accurate and efficient modelling techniques. This study reviews the most commonly used conventional numerical, machine learning, and deep learning models for predicting GWL. Significant advancements have been made in terms of prediction efficiency over the last two decades. However, while researchers have primarily focused on predicting monthly, weekly, daily, and hourly GWL, water managers and strategists require multi-year GWL simulations to take effective steps towards ensuring the sustainable supply of groundwater. In this paper, we consider a collection of state-of-the-art theories to develop and design a novel methodology and improve modelling efficiency in this field of evaluation. We examined 109 research articles published from 2008 to 2022 that investigated different modelling techniques. Finally, we concluded that machine learning and deep learning approaches are efficient for modelling GWL. Moreover, we provide possible future research directions and recommendations to enhance the accuracy of GWL prediction models and improve relevant understanding. groundwater levels (GWL); machine learning; deep learning; conventional methods; forecasting; water level; groundwater; neural networks; review; modflow +
Footnote †: journal: Medical  
superficial artificial neural networks (ANNs), often called multilayer perceptrons (MLPs), with two or more hidden layers. ANNs having one hidden layer known as feed-forward neural networks (FFNNs) have been the most used model in the early days of AI-based research in hydrological studies [4]. Since the GWL time-series data are quite nonlinear and nonstationary, the capability of ANNs is confined to a limited set of variables. Therefore, the adaptive neuro-fuzzy inference system (ANFIS) was developed to analyze complex systems using a backpropagation algorithm and fuzzy logic [5]. It has been reported that AI (machine learning) models used to simulate GWL have shown better results than the traditional physical and numerical models because the latter needs comprehensive details of the physical properties associated with the aquifers to make a prediction [6]. However, classical machine learning models cannot learn long-term dependencies because they do not have the architecture to maintain prior information to make future predictions. To resolve this problem, researchers investigated recurrent neural networks, including RNS, GRUs, and LSTMs, and wavelet transform pre-processing data analysis to study the temporal dependencies between the multiscale input variables very well [6]. However, the prediction efficiency of the monthly, weekly, daily, and hourly basis simulations improved significantly. However, less improvement in prediction accuracy and work in the literature has been reported on yearly GWL simulation despite knowing that water management requires multi-year assessments to formulate long-term strategies to keep the balance between the supply and demand of the groundwater. [PERSON] et al. [7] proposed advanced studies for water treatment technologies and removing emerging contaminants. The water we consume in homes, commercial settings, or industry goes underground and damages the pure underground water. Wastewater treatment is also playing an essential role in water purification. A novel technology called \"Reverse osmosis technology\" is widely used on a massive scale for groundwater treatment [8]. Another experimental study on CO2 utilization in water treatment systems is based on the membrane for reducing the capability of ionic precipitation on the membrane surface and successive level expansion [9].