### Review on Groundwater Level Prediction Using Ensemble Learning  
Due to the growing demand for the more accurate prediction of groundwater levels, the ensemble model has presented a quality evaluation which is essential for the precise identification of influential parameters. A machine learning-based ensemble model was analyzed by [PERSON] et al. Ref. [17] proposed an ensemble-based model using boosted regression tree (BRT) and random forest (RF) to estimate the groundwater hardness. [PERSON] et al. [18] proposed a machine learning-based ensemble model with ANN, SVM, and response surface regression to perform the weak-strong learner's decision-making process. [PERSON] et al. [19] proposed a multi-model perturbation-based algorithm to perturb the feature space using bootstrap sampling to provide a better solution. [PERSON] et al. [20] proposed a design with GAMBoost and AdaBoost for a boosting method, and their bagging method was designed with a classification and regression tree (CART) and RF mode. [PERSON] et al. [21] proposed a DL-based ensemble model using long short-term memory (LSTM), gated recurrent units (GRU), and a multi-layered perceptron (MLP) for predicting the erythrocyte sediment rate. [PERSON] et al. [22] proposed a DL-based ensemble model designed using a recurrent neural network (RNN) and a fully connected neural network for analyzing future stock movements. [PERSON] et al. [23] developed an ML-based ensemble learning process using ANN, SVR, and M-5 rules to predict energy consumption in buildings. From the previous related work, the following gaps are identified, which motivates the identification of the problem statement to propose the research model. The identified gaps are as follows:  
* Machine learning methods struggle to handle enormous amounts of data in groundwater level prediction for huge geographical regions. * Deep learning techniques have high computation time, and it is difficult to identify the better algorithm with hyperparameters upon feature selection in groundwater level prediction. * Identifying the primary attributes for groundwater prediction is tedious, as every attribute has its own advantages and disadvantages that affect the generalization mechanism. * Data pre-processing techniques used in the previous research led to information loss and, in turn, reduced the prediction accuracy. One of the challenging tasks in prediction depends on understanding the data pattern. Even though smart techniques help us with learning adaptability, the selection of an algorithm with the model's potentiality depends on the evaluation of performance metrics with less information loss. As the groundwater level is characterized by various factors such as geological, topological, and hydrological properties, it is mandatory to pre-process the data to maintain data consistency. These considerations and limitations motivate us to identify the significant attributes using the variance inflation factor (VIF) using the multi-collinearity test. The reduced dataset is further approximated using randomized low-rank approximation (RLRA) to reduce the computation cost of the training process. Thus, the proposed model uses RLRA and VIF for data consistency, whereas a deep ensemble model (DEM) using double-edge bi-directed LSTM (DEBi-LSTM) provides improved prediction accuracy.