Groundwater Level Modeling Research Articles

Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial Intelligent

Groundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the important tools for groundwater level modeling. However, the effectiveness of the above-based model in the plains of the arid zone remains underexplored. Fortunately, soil salinity and soil moisture may provide an optimized solution for GWL prediction based on the application of apparent conductivity (ECa, mS/m) using electromagnetic induction (EMI). This has not been attempted in previous studies in oases in arid regions. The study proposed two strategies to predict GWL, included an ECa-based GWL model and a remote sensing-based GWL model (RS_GWL), and then compared and explored their performances and cooperation possibilities. To this end, this study first constructed the ECa prediction model and the RS_GWL with ensemble machine learning algorithms using environmental variables and field observations (474 ECa reads and 436 groundwater level observations from a mountain–oasis–desert system, respectively). Subsequently, a strategy to improve the prediction accuracy of GWL was proposed by comparing the correlation between GWL observations and the two models. The results showed that the RS_GWL prediction model explains 30% and 90% of the spatial variability in the two value domain intervals, GWL < 10 m and GWL > 10 m, respectively. The R2 of the modeling and the validation of the ECa was 79% and 73%, respectively. Careful analysis of the scatter plots between predicted ECa and GWL revealed that when ECa varies between 0–600 mS/m, 600–800 mS/m, 800–1100 mS/m, and >1100 mS/m, the fluctuation ranges of the corresponding GWL correspond to 0–31 m, 0–15 m, 0–10 m, and 0–5 m. Finally, combining the spatial variability of ECa and RS_GWL spatial distribution map, the following optimization strategies were finally established: GWL < 5 m (in natural land with ECa > 1100 mS/m), GWL < 5 m (occupied by farmland from RS_GWL) and GWL > 10 m (from RS_GWL), and 3 < GWL < 10 m (speculated). In conclusion, this study has demonstrated that the integration of EMI technology has significantly improved the precision of forecasting shallow GWL in oasis plain regions, outperforming the outcomes achieved by the use of remote sensing data alone.

An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications

As a critical component of sustainable water management, groundwater level prediction plays a vital role in mitigating droughts and ensuring adequate water supply. For decades, groundwater level dynamics have been primarily studied through physics‐based models, solving partial differential equations. However, interest has increased over the past few years in using Machine Learning (ML) approaches, like Deep Learning (DL) techniques, to study groundwater fluctuation dynamics more efficiently. DL models utilize complex algorithms to identify patterns that may be difficult to observe with traditional physics‐based models, specifically where the underlying physics is complex or poorly understood or where the available physical model is too simple. The article provides an overview of the literature published since 2001, encompassing 91 works that employed ML models to investigate groundwater‐related issues. Within this body of literature, 47 articles employed ML for groundwater level (GWL) modeling. Later, this article delves specifically into the latest advancements in DL for modeling GWL, including recurrent neural network (RNN), long short‐term memory (LSTM), and gated recurrent unit (GRU), and discusses their technical promising performance and advantages. We found that the most used time scale was monthly, which appeared in 18 articles, followed by the daily time scale, which appeared in 13 articles. The authors of the articles used normalization as a feature scaling method in 18 articles, while standardization was used in 3 articles. Python was the predominant programming language used in 18 studies for developing machine learning models, followed by MATLAB, which was used in 5 articles. Most authors divided their data sets into 60–90% for training and 10–40% for testing. Most studies have focused on pure academic research rather than practical industrial applications. Therefore, this article identifies shortcomings in recent literature on DL for GWL studies and suggests addressing these issues to improve practical application in real‐world settings.

A novel committee-based framework for modeling groundwater level fluctuations: A combination of mathematical and machine learning models using the weighted multi-model ensemble mean algorithm

water level (GWL) fluctuations simulation can be divided into three general categories: Analytical solution, conceptual or physical-based models, and data-based or data-driven models. The goal of this research is to develop the MODFLOW conceptual model and the GMDH data-driven model as single models (SMs) for GWL modeling, and then to combine these models to develop a novel mathematical-machine learning model based on a weighted multi-model ensemble mean approach (MMWEM) for a more accurate GWL simulation. For this purpose, the Miqan aquifer, located in northeastern Iran, was selected as a case study. Hydrological and hydrogeological data of the Miqan wetland for ten years (September 2013 to August 2023) with a monthly time step were used to compile models. First, the MODFLOW model was developed for one month (steady-state), then calibrated for nine years (unsteady-state), and finally tested for one year. In the second step, the GMDH model was developed. The grasshopper optimization algorithm (GOA) and the singular value decomposition (SVD) algorithms were used to determine the Ivakhnenko polynomial coefficients of the GMDH (SVD-GMDH and GOA-GMDH). 80% of the data was used to develop SVD-GMDH and GOA-GMDH, and other data was used to test models. Consequently, the sequential forward floating selection (SFFS) method was used to select the best input variables for the SVD-GMDH and GOA-GMDH. The coefficient of determination (R2), root mean squared error (RMSE), and Nash-Sutcliffe Efficiency coefficient (NSE) were used to evaluate the performance of the models. Based on the results of this study, using each of the SMs, GWL can be simulated with acceptable accuracy. However, a closer examination of the results of SMs showed that the MODFLOW has the worst performance among SMs, while the GOA-GMDH has the best function for simulating GWL. In the third step, SMs was used to develop the MMWEM. The GWL of 13 of the 34 piezometers studied in this study was better simulated with the MODFLOW and GOA-GMDH than the SVD-GMDH. Therefore, these models were used to develop the MMWEM for these piezometers. For GWL simulation in 21 piezometers, the SVD-GMDH and GOA-GMDH perform better than the MODFLOW, so these models were used to develop the MMWEM in these piezometers. Finally, the results of the SMs Moreover, the MMWEM were compared with each other. Based on the results of this study, the MMWEM performs better than SMs. In many studies, only the performance of each numerical model and machine learning to simulate groundwater level (GWL) fluctuations is evaluated. Then, their results are compared with each other. However, according to this study, the combination of the results of these models is a big step in improving the accuracy of the groundwater level simulation process. In addition to simulating the groundwater level, this method can simulate other quantitative and qualitative parameters of water resources.

Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods

Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods

De-noising groundwater level modeling using data decomposition techniques in combination with artificial intelligence (case study Aspas aquifer)

Considering the recent significant drop in the groundwater level (GWL) in most of world regions, the importance of an accurate method to estimate GWL (in order to obtain a better insight into groundwater conditions) has been emphasized by researchers. In this study, artificial neural network (ANN) and support vector regression (SVR) models were initially employed to model the GWL of the Aspas aquifer. Secondly, in order to improve the accuracy of the models, two preprocessing tools, wavelet transform (WT) and complementary ensemble empirical mode decomposition (CEEMD), were combined with former methods which generated four hybrid models including W-ANN, W-SVR, CEEMD-ANN, and CEEMD-SVR. After these methods were implemented, models outcomes were obtained and analyzed. Finally, the results of each model were compared with the unit hydrograph of Aspas aquifer groundwater based on different statistical indexes to assess which modeling technique provides more accurate GWL estimation. The evaluation of the models results indicated that the ANN model outperformed the SVR model. Moreover, it was found that combining these two models with the preprocessing tools WT and CEEMD improved their performances. Coefficient of determination (R2) which indicates model accuracy was increased from 0.927 in the ANN model to 0.938 and 0.998 in the W-ANN and CEEMD-ANN models, respectively. It was also improved from 0.919 in the SVR model to 0.949 and 0.948 in the W-SVR and CEEMD-SVR models, respectively. According to these results, the hybrid CEEMD-ANN model is found to be the most accurate method to predict the GWL in aquifers, especially the Aspas aquifer.

Temporal and Spatial Modeling of Groundwater Level in Bushehr Plain using Artificial Intelligence and Geostatistics

Temporal and Spatial Modeling of Groundwater Level in Bushehr Plain using Artificial Intelligence and Geostatistics

Using machine learning algorithms to predict groundwater levels in Indonesian tropical peatlands

Tropical peatlands play a vital role in the global carbon cycle as large carbon reservoirs and substantial carbon sinks. Indonesia possesses the largest share (65 %) of tropical peat carbon, equal to 57.4 Gt C. Human perturbations such as extensive logging, deforestation and canalization exacerbate water losses, especially during dry seasons, when low precipitation and high evapotranspiration rates combine with the increased drainage to lower groundwater levels. Drying and increasing temperatures of the surface peat exacerbate ignition and wildfire risks within the peat soils. As such, it is critically important to know how groundwater levels in peatlands are changing over space and time. In this study, a multilinear regression model as well as two machine learning algorithms, random forest and extreme gradient boosting, were used to model groundwater level over the study period (2010−12) within a peat dome impacted by drainage canals and multiple wildfires in Central Kalimantan, Indonesia. Although all three models performed well, based on overall fit, spatial modeling of groundwater level results revealed that extreme gradient boosting (R2 = 0.998, RMSE = 0.048 m) outperformed random forest (R2 = 0.997, RMSE = 0.054 m) and multilinear regression (R2 = 0.970, RMSE = 0.221 m) near drainage canals, which are key fire ignition risk locations in the peatlands. Our study also shows that, on average, elevation and precipitation are the most important parameters influencing groundwater level spatiotemporally.

Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method

Model development in groundwater simulation and physics informed deep learning (DL) has been advancing separately with limited integration. This study develops a general hybrid model for groundwater level (GWL) simulations, wherein water balance-based groundwater processes are embedded as physics constrained recurrent neural layers into prevalent DL architectures. Because of the automatic parameterizing process, physics-informed deep learning algorithm (DLA) equips the hybrid model with enhanced abilities of inferring geological structures of catchment and unobserved groundwater-related processes implicitly. The main purposes of this study are: 1) to explore an optimized data-driven method as alternative to complicated groundwater models; 2) to improve the awareness of hydrological knowledge of DL model for lumped GWL simulation; and 3) to explore the lumped data-driven groundwater models for cross-region applications. The 91 illustrative cases of GWL modeling across the middle eastern continental United States (CONUS) demonstrate that the hybrid model outperforms the pure DL models in terms of prediction accuracy, generality, and robustness. More specifically, the hybrid model outperforms the pure DL models in 78 % of catchments with the improved Δ NSE = 0.129. Meanwhile, the hybrid model simulates more stably with different input strategies. This study reveals the superiority and powerful simulation ability of the DL model with physical constraints, which increases trust in data-driven approaches on groundwater modellings.

Study of water resources parameters using artificial intelligence techniques and learning algorithms: a survey

Qualitative analysis of water resources is one of the most widely used topics in water resources research today. Researchers use various analysis methods of water parameters to achieve the desired goals in this field. This research uses artificial intelligence (AI), learning machine (LM), data mining, and mathematical techniques to simulate water behavior and estimate its parametric changes. The proposed model used in this study was a Self-adaptive Extreme learning machine (SAELM) to estimate hydrogeological parameters of the Meghan wetland located in Markazi province in Iran. In addition, SAELM simulation results were compared to Least square support vector machine (LSSVM), Multiple linear regression (MLR), and Adaptive Neuro-fuzzy inference system (ANFIS) models. The simulated parameters were Electrical Conductivity (EC), Total Dissolved Solids (TDS), Groundwater Level (GWL), and salinity. This information was related to sampling for 175 months in the study area. Finally, after simulation operation, four models were introduced as superior models. Mentioned exceptional models were SAELM in GWL modeling, SAELM in modeling the EC, MLR in salinity simulation, and LSSVM in the simulation of TDS parameters. Moreover, by five approaches, the models' performance was evaluated. Suggested strategies were performance evaluation by statistical indicators, Wilson score method uncertainty analysis (WSMUA), response & correlation plots, discrepancy ratio charts, and distribution error diagrams. Based on statistical indicators, the SAELMGWL model was the most accurate model with RMSE, MAPE, and R2 indices equal to 0.1496, 0.0043, and 0.9933, respectively. The ANFIS model had the worst results in simulation.

Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis

Groundwater is a vital source of freshwater, supporting the livelihood of over two billion people worldwide. The quantitative assessment of groundwater resources is critical for sustainable management of this strained resource, particularly as climate warming, population growth, and socioeconomic development further press the water resources. Rapid growth in the availability of a plethora of in-situ and remotely sensed data alongside advancements in data-driven methods and machine learning offer immense opportunities for an improved assessment of groundwater resources at the local to global levels. This systematic review documents the advancements in this field and evaluates the accuracy of various models, following the protocol developed by the Center for Evidence-Based Conservation. A total of 197 original peer-reviewed articles from 2010–2020 and from 28 countries that employ regression machine learning algorithms for groundwater monitoring or prediction are analyzed and their results are aggregated through a meta-analysis. Our analysis points to the capability of machine learning models to monitor/predict different characteristics of groundwater resources effectively and efficiently. Modeling the groundwater level is the most popular application of machine learning models, and the groundwater level in previous time steps is the most employed input data. The feed-forward artificial neural network is the most employed and accurate model, although the model performance does not exhibit a striking dependence on the model choice, but rather the information content of the input variables. Around 10–12 years of data are required to develop an acceptable machine learning model with a monthly temporal resolution. Finally, advances in machine and deep learning algorithms and computational advancements to merge them with physics-based models offer unprecedented opportunities to employ new information, e.g., InSAR data, for increased spatiotemporal resolution and accuracy of groundwater monitoring and prediction.

Groundwater level prediction using machine learning models: A comprehensive review

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.

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.

Application of artificial intelligence in groundwater ecosystem protection: a case study of Semnan/Sorkheh plain, Iran

Groundwater ecosystems have unparalleled environmental value. Accurate modeling of groundwater level (GWL) fluctuations is a vital requirement for the protection of the groundwater ecosystems. The GWL modeling is a challenge due to complexities of the underground geological structure. Among the various modeling methods, artificial intelligence (AI)-based approaches serve as desirable alternatives due to their distinctive and potent properties. One of the most practical AI-based approaches is an artificial neural network (ANN) model. The purpose of the current study was to apply time delay neural networks (TDNN) with different network structures and input delays to model the GWL fluctuations. The variables used in the construction and validation of the models were average weekly GWL from January 2002 to January 2013 in two monitoring sites in Semnan/Sorkheh plain, Iran. The study area is an arid region, where overutilization of groundwater threatens the water security in this area. The computational results of the current research demonstrated that the TDNN model is a practical tool in modeling time-series GWL compared to the other state-of-the-art AI-based approaches. Future studies are recommended to explore application of proposed model for more sustainable and effective Groundwater Resources Management (GWRM).

군집 분석과 지하수위 모델을 이용한 인위적 하천수위 조절에 따른 지하수위 영향 정량적 평가

하천 정비 사업을 통한 보 건설 등으로 하천수위를 인위적으로 제어할 수 있게 됨으로써, 이로 인해 발생하는 지하수계의 변화가 유역의 자연환경과 농업 활동에 어떠한 영향을 미치는가에 대하여 보다 자세한 이해가 필요하게 되었다. 이러한 이해를 위해서는 하천 유역에서 지하수의 수량과 수질에 영향을 미칠 수 있는 지표수 - 지하수 상호 작용에 대한 통찰을 바탕으로, 지하수위 변화의 다양한 원인을 정량적으로 분석하는 것이 우선시 되어야 하며, 이를 바탕으로 적절히 관련 정책 등에 반영할 필요가 있다. 따라서, 본 연구에서는 하천수위 변화에 따른 인근 지하수 수위를 군집 분석을 통해 연구 지역 내 지하수를 변화 패턴의 유사성에 따라 분류하고, 지하수위 모델을 이용해 지하수위 변동을 일으키는 자연적/ 인공적 요인의 기여도를 개별적으로 정량화하고자 하였다. 또한 다양한 모델 시나리오를 통해 하천수위 조절 혹은 양수와 같은 인위적인 요소가 지하수위 변화에 미치는 영향을 분석하였다. 연구에 사용한 모델링 기법은 지하수위 변화를 다중 요인에 의해서 일어나는 변화의 합으로 가정하고, 이를 요소별 물리적 모형을 이용해 구한 합성 지하수위를 실측 지하수위와 비교하여 모델 파라메터를 추정하였다. 이렇게 최적화된 모델을 이용하여 지하수위 총 변화량 중에서 강우량, 하천수위, 양수량 등 각각의 요인이 미치는 영향을 개별적으로 정량화하였다. 군집 분석 결과, 연구 지역의 지하수는 상호 유사성이 높은 두개의 그룹으로 나눌 수 있고, 이러한 차이는 주로 하천과의 지리적인 거리에 기인한 것으로 나타났다. 한편, 지하수위 모델을 통한 분석 결과 하천 수위 조절 기간 중 지표수 수위 하강이 지하수위 하강에 기여하는 비율은 시간에 따라 약 50% ~ 90%로 나타나고, 하천수위 조절에 의한 지하수위 하강은 최대 2.0 m, 양수에 의해서는 최대 0.5 m의 추가적인 지하수위 하강이 발생한 것으로 분석되었다. 향후 이러한 연구 결과는 하천 환경을 유지하면서도 농업 활동을 위축시키지 않는 최적의 하천 관리 방안을 수립하는데 활용할 수 있을 것이다.

Geostatistical based framework for spatial modeling of groundwater level during dry and wet seasons in an arid region: a case study at Hadat Ash-Sham experimental station, Saudi Arabia

Saudi Arabia (SA) lies in an arid region where groundwater is the main natural resource; therefore, it is essential to understand the groundwater dynamics for the best groundwater management practice in SA. In Hadat Ash-Sham Farm Experimental Station, SA, water table data from 11 wells and rainfall data were monitored for 16 months. The water table (WT) data is analyzed using the geostatistical method with the ordinary Kriging technique to generate the best WT spatial distribution map for each month and the expected flow direction. The cross-validation technique is used to evaluate the goodness of the developed WT maps. The Kriging maps show two regimes: weak spatial dependence (WSD, the ratio of the nugget to sill > 75%) and strong spatial dependence (SSD, the ratio of the nugget to sill < 25%). The WSD regime happens during dry seasons, while the SSD happens during wet seasons. The SSD gives better results and accuracy when compared to WSD. The root-mean-square error (RMSE) of WT varies between 0.26 and 3.4 m in the case of SSD, while it varies between 0.51 and 4.8 m in the case of WSD. WT maps show that the groundwater flow direction is from south-east to north-west during the wet season (SSD). This direction is in the orientation of surface stream with higher elevation (in the south) to the surface stream with lower elevation (in the north), where the study area is between these surface streams. While during the dry season (WSD), there is no preferred direction since there is almost no flow.

Combining Group Method of Data Handling with Signal Processing Approaches to Improve Accuracy of Groundwater Level Modeling

Groundwater level forecasting is a paramount necessity for integrated management of a basin. Development of suitable models is an essential step in determining groundwater level fluctuations in the future. The main objective of this study was to provide a powerful hybrid model by combining the group method of data handling (GMDH) and certain signal processing techniques, i.e., ensemble empirical mode decomposition (EEMD), wavelet transform (WT) and wavelet packet transform (WPT) for groundwater level forecasting in monthly time steps. Two different plains were selected to assess the performance of the afore-mentioned methods. The results showed that all of these preprocessing methods improved the capability of the group method of data handling model. The EEMD–GMDH model outperformed the WT–GMDH model. The WPT–GMDH model had a superior performance compared to both of the afore-mentioned hybrid models. However, it was shown that WPT–GMDH model had more computational cost that may affect the feasibility of this modeling approach in some cases. Finally, the EEMD–GMDH model can be introduced as a suitable modeling approach for groundwater level forecasting.

Application of a Novel Hybrid Wavelet-ANFIS/Fuzzy C-Means Clustering Model to Predict Groundwater Fluctuations

In order to optimize the management of groundwater resources, accurate estimates of groundwater level (GWL) fluctuations are required. In recent years, the use of artificial intelligence methods based on data mining theory has increasingly attracted attention. The goal of this research is to evaluate and compare the performance of adaptive network-based fuzzy inference system (ANFIS) and Wavelet-ANFIS models based on FCM for simulation/prediction of monthly GWL in the Maragheh plain in northwestern Iran. A 22-year dataset (1996–2018) including hydrological parameters such as monthly precipitation (P) and GWL from 25 observation wells was used as models input data. To improve the prediction accuracy of hybrid Wavelet-ANFIS model, different mother wavelets and different numbers of clusters and decomposition levels were investigated. The new hybrid model with Sym4-mother wavelet, two clusters and a decomposition level equal to 3 showed the best performance. The maximum values of R2 in the training and testing phases were 0.997 and 0.994, respectively, and the best RMSE values were 0.05 and 0.08 m, respectively. By comparing the results of the ANFIS and hybrid Wavelet-ANFIS models, it can be deduced that a hybrid model is an acceptable method in modeling of GWL because it employs both the wavelet transform and FCM clustering technique.

Groundwater level modeling with hybrid artificial intelligence techniques

It is necessary to properly simulate groundwater levels in order to ensure an adequate management of scarce water resources. However, simulating groundwater levels accurately is one of the challenging issues in hydrology because of its complex system. In the current study, Gene Expression Programming (GEP) and M5 model tree (M5) are combined with Ensemble Empirical Mode Decomposition (EEMD) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) methods for pre-processing input data to produce hybrid models for groundwater simulation. The performance of hybrid models is compared with the outputs of sole GEP and M5 and their counterparts combined with Wavelet transform (WT). The results indicate that pre-processing can improve the performance of the simple models and WT as well as CEEMD are better than EEMD to produce hybrid models. By employing pre-processing techniques, the improvement of R2 for GEP and M5, regarding Multiple Linear Regression (MLR) as a benchmark, is 13.11%, 6.65% and 8.20% for EEMD-GEP, CEEMD-GEP and CEEMD-M5 respectively, while it is –3.28% for EEMD-M5 for the first data set. For the second data set, the improvement of EEMD-GEP = 103.23%, CEEMD-GEP = 125.81% and CEEMD-M5 = 77.42%, and for the third data set, EEMD-GEP = 76%, CEEMD-GEP = 80% and CEEMD-M5 = 54%. In the second and third data set, the performance of EEMD-M5 decreases again. According to the results, hybrid GEP shows the best performance for groundwater water simulation, while M5 combined with EEMD is not recommended for the simulation due to its weak performance.

Future Projected Groundwater Level Trend Analysis using GIS Techniques

High Population Growth and increase the urban sprawl in earth to less the groundwater resources causes degradation of groundwater Level in India. Water is very important for our life and natural resources. There are various uses of groundwater just as drinking, irrigation, and manufacturing. Today we are facing the shortage of groundwater level. The main goal of study is, because of increase in the population there is increase the number of bore well in earth surface this the reason to declination of groundwater level, so in these study to analysis of spatio-temporal changes in groundwater level fluctuation in Bhopal district in Madhya Pradesh, India. According to censes in Huzur Tehsil, M.P, Bhopal District, in 2001 population is 1429132 and 2011 population is 1834493. Collected the total of 350 observation wells Huzur Tehsil, Bhopal district for point observations. And using the GIS techniques spatial Analysis tools in ArcGIS10.5 to find out different Change of Groundwater Level Fluctuation since period (1996-2015) and using Matlab to generate the GWL (groundwater level) modeling .Generate the Future projected Groundwater level Fluctuation(2016-2050) and Trend Analysis of Groundwater Level (1996-2015) and Future Projected Groundwater level (2016-2050).

Potential impacts of climate change on groundwater level through hybrid soft-computing methods: a case study-Shabestar Plain, Iran.

Groundwater aquifers have always been confronted with significant challenges around the world such as climate change, over-extraction, pollution by wastewaters, and saltwater intrusion in coastal areas. Prediction of groundwater level under the effects of climate change is more important in water resource management. This study has therefore been evaluated the effects of two climate parameters (i.e., precipitation and temperature) in groundwater level for the Shabestar Plain, Iran. For this end, four models from General Circulation Models (GCM) were then used to evaluate future climate change scenarios of the Representative Concentration Pathway (i.e., RCP2.6, RCP4.5, RCP8.5). In the next phase, to reduce the spatial complexity of observation wells, clustering analysis was used. In case of groundwater level modeling, time series in the base period, Least Square Support Vector Machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Nonlinear Autoregressive Network with Exogenous inputs (NARX) were also used. To improve the prediction accuracy, time series preprocessing made by wavelet-based de-noising approach was used. Analysis of the results illustrates an increase in temperature and a decrease in precipitation for study region in the future period times. The results also reveal that hybrid techniques of the wavelet-NARX give best results in comparison with the other models. A simulation result illustrates that the groundwater level declines in RCP2.6, 4.5, and 8.5, which gives average levels of 0.61, 0.81, and 1.53m, respectively, for the future period years (i.e., 2020-2024). These results would lead to continuous groundwater depletion. These findings emphasize the necessity of the importance of extraction policies in water resource management.