Groundwater Level Prediction Research Articles

Study on the Exploitation Scheme of Groundwater under Well-Canal Conjunctive Irrigation in Seasonally Freezing-Thawing Agricultural Areas

The suitable groundwater exploitation scheme in freezing-thawing agricultural areas under the well-canal conjunctive irrigation conditions is confronted with two major challenges, which are computationally expensive local grid refinements along wells, and the model suitability problem in the freezing-thawing period. In this study, an empirical method for groundwater level prediction in the freezing-thawing period was developed and integrated with the local grid refinement groundwater model MODFLOW-LGR for the groundwater process prediction. The model was then applied to estimate the suitable groundwater exploitation scheme, including the size of well-irrigated area and the irrigation area of single well. The results showed that suitable size of well-irrigated area should be smaller than 15 × 106 m2, and the recommended irrigation area of single well as 15 × 104 m2 to 19 × 104 m2. The recommended layout parameters of groundwater exploitation were further used to plan the well-canal conjunctive irrigation scheme in Yongji irrigation district located in northern China. This study provides an important pilot example of the conjunctive use of groundwater and surface water in arid irrigation areas with a seasonal freezing-thawing period.

Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China

Abstract Dynamic monitoring data of groundwater level is an important basis for understanding the current situation of groundwater development and for the utilization and planning of sustainable exploitation. Dynamic monitoring data of groundwater level are typical spatio-temporal sequence data, which have the characteristics of non-linearity and strong spatio-temporal correlation. The trend of dynamic change of groundwater level is the key factor for the optimal allocation of groundwater resources. However, most of the existing groundwater level prediction models are insufficient in considering temporal and spatial factors and their spatio-temporal correlation. Therefore, construction of a space–time prediction model of groundwater level considering space–time factors and improving the prediction accuracy of groundwater level dynamic changes is of considerable theoretical and practical importance for the sustainable development of groundwater resources utilization. Based on the analysis of spatial–temporal characteristics of groundwater level of the pore confined aquifer II of Changwu area in the Yangtze River Delta region of China, the wavelet transform method was used to remove the noise in the original data, and the K-nearest neighbor (KNN) method was used to calculate the water level. The spatial–temporal dataset and the long short-term memory (LSTM) were reconstructed by screening the spatial correlation of the monitoring wells in the study area. A spatio-temporal KNN-LSTM prediction model for groundwater level considering spatio-temporal factors was also constructed. The reliability and accuracy of KNN-LSTM, LSTM, support vector regression (SVR), and autoregressive integrated moving average (ARIMA) model were evaluated by a cross-validation algorithm. Results showed that the prediction accuracy of KNN-LSTM is 20.68%, 46.54%, and 55.34% higher than that of the other single prediction models (LSTM, SVR, and ARIMA, respectively).

Comparing four machine learning model performances in forecasting the alluvial aquifer level in a semi-arid region

Groundwater level fluctuation is a nonlinear and non-stationary system as it depends on several factors in the time and space scales. Conceptual models require several physical parameters whose estimation is delicate in poorly monitored areas. However, data-based models may be valuable for modelling and forecasting groundwater level over short and long terms. To that end, four machine learning models, namely: Support Vector Regression, k- Nearest Neighbour (k-NN), Random Forest (RF), and Artificial Neural Network (ANN), are trained, validated, and compared for predicting groundwater level (GWL) at seven piezometers on alluvial groundwater of Tanobart aquifer in Morocco. The results revealed that the ANN models succeeded properly in simulating GWL at five piezometers out of the total seven piezometers considered in this study (NSE = 0.69 to 0.8); the RF was satisfactory at five piezometers (NSE = 0.41 to 0.72) and SVR at three piezometers (NSE = 0.57 to 0.81); the k-NN was the poorest model among all the investigated models (NSE = −1.05 to −0.15). The uncertainty analysis showed that the selected models are accurate overall; the SVR model showed the best forecasting accuracy with the smallest 95% interval prediction error (−0.25 m and 0.11 m) at one piezometer. This study provides new insight to forecast the GWL under a semi-arid context such Tanobart aquifer in Khemesset province, Morocco.

Analysis of Groundwater Level Prediction Performance with Influencing Factors by Artificial Neural Network

Analysis of Groundwater Level Prediction Performance with Influencing Factors by Artificial Neural Network

Groundwater level modeling framework by combining the wavelet transform with a long short-term memory data-driven model

Developing models that can accurately simulate groundwater level is important for water resource management and aquifer protection. In particular, machine learning tools provide a new and promising approach to efficiently forecast long-term groundwater table fluctuations without the computational burden of building a detailed flow model. This study proposes a multistep modeling framework for simulating groundwater levels by combining the wavelet transform (WT) with the long short-term memory (LSTM) network; the framework is named the combined WT-multivariate LSTM (WT-MLSTM) method. First, the WT decomposes the groundwater level time series (i.e., the training stage) into a self-control term and a set of external-control terms. Second, Pearson correlation analysis reveals the correlations between the influencing factors (i.e., river stage) and the groundwater table, and the multivariate LSTM model incorporating external factors is built to simulate the external-control terms. Third, the spatiotemporal evolution of the groundwater level is modeled by reconstructing the sequence of each term of the groundwater level time series. Methodological applications in the Liangshui River Basin, Beijing, China and the Cibola National Wildlife Refuge along the lower Colorado River, United States, show that the combined WT-MLSTM model has a higher simulation accuracy than the standard LSTM, MLSTM, and WT-LSTM models. A comparison between the combined WT-MLSTM model and support vector machine (SVM) also demonstrates the advantage of the proposed model. Additional comparison between model forecasts and observed groundwater levels shows the model predictability for short-term time series. Further analysis reveals that the applicability of the combined WT-MLSTM model decreases with increasing distance between the groundwater well and adjacent river channel, or with the increasing complexity of the changing groundwater level patterns, which may be driven by additional controlling factors. This study therefore provides a new methodology/approach for the rapid and accurate simulation and prediction of groundwater level.

Forecasting of water table fluctuation for Priyadarshini watershed using Ann with different algorithms

Groundwater is an important natural resource essential for sustenance of life. Over 98% of the freshwater on the Earth lies below its surface. The ANN technique is applied as a new approach and an attractive tool to study and predict groundwater levels without applying physically based hydrologic parameters. It is observed that the maximum LM value of R for training and validation are 0.908 and 0.903 shown in well 2 (2-9-1) and testing is 0.949 shown in well 8 (2-9-1), whereas minimum value for training is 0.684 shown in well 1 (4-4-1) for validation and testing are 0.159 and 0.773 well 9 (3-5-1) The maximum observed R value for CG training, validation and testing are 0.76 shown in well 3 (4-5-1), 0.85 shown in well 9(3-5-1) and 0.891 shown in well 7 (2-5-1) whereas the minimum value for training and testing are 0.671 and 0.458 shown in well 1 (4-4-1) for validation the minimum R value is 0.638 well 2 (4-7-1). Considering training, validation and testing period and all the statistics it is difficult to say which algorithm is better among the two selected for study. Because there was a lot of variation in all the statistics among the two selected algorithms for training, validation and testing period. But considering the testing period of all the nine wells it was found that LM algorithm was better than CG for wells i.e., well 1 (2-9-1), well 2 (2-9-1), well 3 (1-8-1), well 4 (1-6-1), well 5 (2-9-1), well 6 (1-9-1), well 8 (2-9-1) while CG algorithm was better than LM for wells i.e., well 7 (2-5-1) and well 9 (3-5-1) So these algorithms for particular well were selected for sensitivity analysis.

The response of groundwater to multiple concerning drivers and its future: a study on Birbhum District, West Bengal, India

Groundwater and its upcoming crisis are the present-day concern of the scientist. This research mainly focuses on responses of groundwater dynamicity to some important drivers, viz. agricultural yield, groundwater irrigated area, groundwater draft, landuse/landcover, and stage of development. The result of this study has been done under three sections. In the first section, the spatiality of groundwater has been discussed where it has been noticed that the western side of the district groundwater level is near the surface due to low drafting and low agricultural yield. Moreover, hard rock geology in the western part disappoints the drilling process. On the eastern part, rich alluvial soil influences high agricultural yield hence groundwater level lowering down rapidly. In the second section, the nature of groundwater levels has been analyzed through the boxplot, and cluster diagram, where boxplots have been drawn over different geological facies, which depicts groundwater is highly fluctuating in hard clay geology. For example, high agricultural intensity and high groundwater draft is the characteristic feature of hard clay geology. The dendrogram in cluster analysis represents a homogeneous groundwater level fluctuating station in three different time series. Last section deals with the future of groundwater level where an artificial neural network (ANN) model has been applied to extract the predicted groundwater level for 2030. This type of environmental analysis, such as groundwater fluctuations in relation to different sensitive parameters and the use of a machine learning model, would aid potential researchers and communities in making wise groundwater use decisions.

Groundwater Level Forecast Via a Discrete Space-State Modelling Approach as a Surrogate to Complex Groundwater Simulation Modelling

Reliable and precise forecasts of future groundwater level fluctuations are crucial constituents of sustainable management of scarce water resources and design of remediation plans. Groundwater simulations and predictions are often performed by employing physically based models, which are not applicable in a majority of water scarce areas around the globe, particularly in the developing countries like Bangladesh due to data limitations. On the other hand, data-driven statistical forecast models have demonstrated their suitability to model nonlinear and complex hydrogeological processes to forecast short- and long-term groundwater level fluctuations. The purpose of this effort is to propose a non-physical based approach by utilizing a discrete Space-State model as a prediction tool to forecast future scenarios of groundwater level fluctuations. The present study utilizes the prediction focused approach of the system identification process in which the overall objective is to develop a pragmatic dynamic system model. The performance of the proposed approach is evaluated for groundwater level data at three observation wells of Tanore upazilla in Rajshahi district, Bangladesh. Historical weekly time series data of groundwater level fluctuations from the three observation wells for 39 (1980–2018) years is used to develop the time series model, which is used for future groundwater level predictions for a period of next 22 years (up to 2040). The findings demonstrate the conceivable applicability of the proposed discrete Space-State modelling approach in forecasting future scenarios of groundwater level fluctuations in the selected observation wells.

The prediction of aquifer groundwater level based on spatial clustering approach using machine learning.

Water resources management requires a proper understanding of the status of available and exploitable water. One of the useful management tools is the use of simulation models that are highly efficient in spite of the complex problems in the groundwater sector. In the present study, three data-based models, namely, group method of data handling (GMDH), Bayesian network (BN), and artificial neural network (ANN), have been investigated to simulate the groundwater levels and assess the quantitative status of aquifers. Five observation wells were selected in Birjand aquifer using spatial clustering to analyze and evaluate the aquifer. To determine the effective variables in predicting groundwater level, 10 scenarios were developed by combining several variables, including groundwater level in the previous month, aquifer exploitation, surface recharge, precipitation, temperature, and evaporation. Results showed that the GMDH model with three input variables, i.e., the groundwater level in the previous month, aquifer exploitation, and precipitation, had the highest prediction performance, RMSE, NASH, MAPE, and R2 of which were obtained equal to 0.074, 0.97, 0.0037, and 0.97, respectively. Furthermore, Taylor's diagram showed that the predicted values using the GMDH model had the highest correlation with the observational data. Hydrograph simulation was performed for 6 years to analyze the condition of the aquifer. The results showed that the groundwater level is in critical condition in this aquifer, and a 1.2-m groundwater loss was predicted for this aquifer. The findings of this study show that the management of the studied aquifer is necessary to improve its current situation.

Predictive groundwater levels modelling by Inclusive Multiple Modelling (IMM) at multiple levels

An investigation is presented in this paper to predict Groundwater Level (GWL) by Inclusive Multiple Modelling (IMM) practices, introduced recently by the authors, as a strategy by organising multiple models at three hierarchical levels: Artificial Neural Networks (ANNs), Sugeno Fuzzy Logic (SFL), Neuro-Fuzzy (NF), Support Vector Machine (SVM) and Gene Expression Programming (GEP). The IMM novelty of the study is to investigate a modelling strategy at three hierarchical levels, such that any base models at Level 1 are not reused as the combiner model at Levels 2 and Level 3 and this leads to a number of strategies. The results provide some evidence that (i) combining base models at Levels 2 and 3 enhance the performances compared with those of individual base models at Level 1; and (ii) the results at Level 3 become defensible and thereby suitable for the development of management scenarios. The decline in GWLs is investigated through management scenarios, which show that water use has higher impacts on groundwater level variations in the study area than those by climatic variabilities and this underpins the evidence for the necessity of management plans and strategies for the study area.

Assessment of potential hydrological climate change impacts in Kastoria basin (Western Macedonia, Greece) using EUROCORDEX regional climate models

<p>The Mediterranean region is expected to present reduced availability of water resources due to climate change. This study aims to assess the potential hydrological responses to climate change in the Kastoria basin (Western Macedonia, Northern Greece) for the period 2019-2078. Climate projections from eight regional climate models from EURO-CORDEX were bias-adjusted using the linear scaling method. The bias-adjusted climate data were used to force the FeFLOW hydro-logical model to predict the discharge of the Kastoria aquifer towards lake Orestiada along with the projected groundwater level distribution. Precipitation (temperature) shows a tendency to decrease (increase) mainly in late spring to early autumn while increase (decrease) in the other sea-sons. Moreover, results indicate a significant increase in temperature and a slight decrease in precipitation towards 2078, while the predicted groundwater level of Kastoria aquifer will reduce slightly. However, the future hydrological behavior of the basin indicates a substantial reduction by approximately 15% of total water yield towards the end of the century.</p>

Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia

Groundwater levels have been declining recently in Malaysia. This is why, the current study was aimed to propose an accurate groundwater levels prediction model using machine learning algorithms in highly populated towns in Selangor, Malaysia. The models developed used 11 months of previously recorded data of rainfall, temperature and evaporation to predict groundwater levels. Three machine learning models have been tested and evaluated; Xgboost, Artificial Neural Network, and Support Vector Regression. The results showed that for the first scenario, which had combinations of 1,2 and 3 days delayed of rainfall data only considered as an input, the models’ performance was the worst. while in the second scenario the proposed Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations. A significant increase in performance was achieved in the third scenario, when using 1 day delayed of groundwater levels as an input as well where R 2 equal to 0.92 in the Xgboost model in scenario 3 and 0.16, 0.11 in scenarios 2 and 1 respectively. The results obtained in this study serves as a great benchmark for future groundwater levels prediction using Xgboost algorithm.

Parametric uncertainty analysis on hydrodynamic coefficients in groundwater numerical models using Monte Carlo method and RPEM

Groundwater resources are the only source of water in many arid and semi-arid regions. It is important to manage these resources to have a sustainable development. However, there are many factors influencing the accuracy of the results in groundwater modeling. In this research, the uncertainty of two important groundwater model parameters (hydraulic conductivity and specific yield) were considered as the main sources of uncertainty in estimating water level in an unconfined aquifer, in Iran. For this purpose, a simple method called Rosenblueth Point Estimate Method (RPEM) was used to assess groundwater modeling parametric uncertainty, and its performance was compared with Monte Carlo method as a very complicated and time-consuming method. According to calibrated values of hydraulic conductivity and specific yield, several uncertainty intervals were considered to analyze uncertainty. The results showed that the optimum interval for hydraulic conductivity was 40% increase–30% decrease of the calibrated values in both Monte Carlo and RPEM methods. This interval for specific yield was 200% increase–90% decrease of the calibrated values. RPEM showed better performance using the evaluating indices in comparison with Monte Carlo method for both hydraulic conductivity and specific yield with 43% and 17% higher index values, respectively. These results can be used in groundwater management and future prediction of groundwater level.

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.

Coupling model of groundwater and land subsidence and simulation of emergency water supply in Ningbo urban Area, China

Groundwater withdrawal has caused severe land subsidence in Ningbo since 1964. The land subsidence rate slowed down by the prohibition of groundwater exploitation. However, water supply crisis prompted a new task of how to satisfy the emergency demands of water supply. This study tries to provide insights into the prediction of groundwater level and land subsidence and formulate reasonable exploitation plans for emergency water supply in Ningbo urban area. A coupling model of groundwater and land subsidence was developed to quantify the subsidence caused by water withdrawal. The proposed model is recognized for the period 1989–1992 and is verified for the period 1992–1993 using observed groundwater and land subsidence records in Ningbo urban area. Based on the calibrated and evaluated model, six reasonable exploitation plans for emergency water supply in Ningbo urban area are formulated, and the dynamic prediction of groundwater level and land subsidence changes in each plan are made. Numerical results show that the pore confined water in Ningbo urban area has good supply conditions and strong water level recovery ability, which can meet the demand of short-term centralized water supply. A longer time of emergency water supply and a lower controlling depth of water level leads to larger subsidence. After the completion of water supply, areas near exploited well may appear subsidence rebound due to the recovery of groundwater level, but subsidence continues to increase in areas far away from the exploited well due to the delayed settlement of the lagging interbeds.

Groundwater Level Prediction based on Neural Networks: A case study in Linze, Northwestern China

Groundwater level is an important factor in evaluating groundwater resources. Due to numerous non-linear factors, establishing theoretical models is difficult.. Therefore, this paper proposesthe BP (Back Propagation) neural network and the Radial Basis Function (RBF) neural network. The study area is divided into two zones. The R2 (coefficient of determination) and RMSE (Root Mean Squared Error) are used to evaluate the performance. The BP neural network is used to predict groundwater level in the two zones with the R2of0.57 and 0.54, with the RMSE of 0.0804 meters and 0.1864 meters respectively. The RBF neural network is implemented with R2of 0.65 and 0.61, with RMSE of 0.0720 meters and 0.1519 meters, respectively. The results show the RBF neural network performs better than the BP neural network in the accuracy of predicting groundwater level. This study shows the feasibility and superiority of groundwater simulation using neural network.

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named wavelet-support vector regression (W-SVR) and wavelet-Gaussian process regression (W-GPR)) are used to forecast groundwater level in Semnan plain (arid area) for the next month. Three different wavelet transformations, namely Haar, db4, and Symlet, are tested. Four statistical metrics, namely root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R 2), and Nah-Sutcliffe efficiency (NS), are used to evaluate performance of different methods. The results reveal that SVR with RMSE of 0.04790 (m), MAPE of 0.00199%, R 2 of 0.99995, and NS of 0.99988 significantly outperforms GPR with RMSE of 0.55439 (m), MAPE of 0.04363%, R2 of 0.99264, and NS of 0.98413. Besides, the hybrid W-GPR-1 model (i.e. GPR with Harr wavelet) remarkably improves the accuracy of GWL prediction compared to GPR. Finally, the hybrid W-SVR-3 model (i.e. SVR with Symlet) provides the best GWL prediction with RMSE, MAPE, R2, and NS of 0.01290 (m), 0.00079%, 0.99999, and 0.99999, respectively. Overall, the findings indicate that hybrid models can accurately predict GWL in arid regions.

Predicting groundwater level fluctuations under climate change scenarios for Tasuj plain, Iran

Coastal aquifers world-wide are being confronted with several major challenges, such as overextraction of groundwater, climate change impacts, contamination by wastewater, and saltwater intrusion into water resources. Climate change induced alteration of the hydrological cycle is one of the main threats to future accessibility of water resources. Effective prediction of possible impacts of climate change on groundwater reserves, a crucial water resource, could be of great importance for sustainable water management. In a comparative study, artificial neural network (ANN), least square support vector machine (LSSVM), and nonlinear autoregressive network with exogenous inputs (NARX) models was applied to evaluate possible impacts of three representative concentration pathways (RCP) climate change scenarios (RCP2.6, RCP4.5, RCP8.5) on groundwater levels in Tasuj Plain, Iran. Four general circulation models (GCM) was used to predict temperature and precipitation values for the future period 2022–2050 and found that future temperature increased, while the amount of precipitation decreased. To improve the accuracy of three models in groundwater level prediction, db4 wavelet transform was applied. The results indicated that the Wavelet-NARX approach gave the best accuracy in forecasting groundwater level in the study area. In all cases, prediction indicated that groundwater level in all representative wells would decline in future.

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

Drilling data for groundwater extraction incur changes over time due to variations in hydrogeological and weather conditions. At any time, if there is a need to deploy a change in drilling operations, drilling companies keep monitoring the time-series drilling data to make sure it is not introducing any changes or new errors. Therefore, a solution is needed to predict groundwater levels (GWL) and detect a change in boreholes data to improve drilling efficiency. The proposed study presents an ensemble GWL prediction (E-GWLP) model using boosting and bagging models based on stacking techniques to predict GWL for enhancing hydraulic resource management and planning. The proposed research study consists of two modules; descriptive analysis of boreholes data and GWL prediction model using ensemble model based on stacking. First, descriptive analysis techniques, such as correlation analysis and difference mechanisms, are applied to investigate boreholes log data for extracting underlying characteristics, which is critical for enhancing hydraulic resource management. Second, an ensemble prediction model is developed based on multiple hydrological patterns using robust machine learning (ML) techniques to predict GWL for enhancing drilling efficiency and water resource management. The architecture of the proposed ensemble model involves three boosting algorithms as base models (level-0) and a bagging algorithm as a meta-model that combines the base models predictions (level-1). The base models consist of the following boosting algorithms; eXtreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB). The meta-model includes Random Forest (RF) as a bagging algorithm referred to as a level-1 model. Furthermore, different evaluation metrics are used, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score. The performance of the proposed E-GWLP model is compared with existing ensemble and baseline models. The experimental results reveal that the proposed model performed accurately in respect of MAE, MSE, and RMSE of 0.340, 0.564, and 0.751, respectively. The MAPE and R2 score of our proposed approach is 12.658 and 0.976, respectively, which signifies the importance of our work. Moreover, experimental results suggest that E-GWLP model is suitable for sustainable water resource management and improves reservoir engineering.

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia.