Vulnerability Indexing to Saltwater Intrusion from Models at Two Levels using Artificial Intelligence Multiple Model (AIMM)

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.

Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network

This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.

Quantifying the groundwater total contamination risk using an inclusive multi-level modelling strategy

This paper investigates aggregated risks in aquifers, where risk exposures may originate from different contaminants e.g. nitrate-N (NO₃-N), arsenic (As), boron (B), fluoride (F), and aluminium (Al). The main goal is to develop a new concept for the total risk problem under sparse data as an efficient planning tool for management through the following methodology: (i) mapping aquifer vulnerability by DRASTIC and SPECTR frameworks; (ii) mapping risk indices to anthropogenic and geogenic contaminants by unsupervised methods; (iii) improving the anthropogenic and geogenic risks by a multi-level modelling strategy at three levels: Level 1 includes Artificial Neural Networks (ANN) and Support Vector Machines (SVM) models, Level 2 combines the outputs of Level 1 by unsupervised Entropy Model Averaging (EMA), and Level 3 integrates the risk maps of various contaminants (nitrate-N, arsenic, boron, fluoride, and aluminium) modelled at Level 2. The methodology offers new data layers to transform vulnerability indices into risk indices and thereby integrates risks by a heuristic scheme but without any learning as no measured values are available for the integrated risk. The results reveal that the risk indexing methodology is fit-for-purpose. According to the integrated risk map, there are hotspots at the study area and exposed to a number of contaminants (nitrate-N, arsenic, boron, fluoride, and aluminium).

Evaluation of seawater intrusion and water quality prediction in Dagu River of North China based on fuzzy analytic hierarchy process exponential smoothing method

It is of great significance to evaluate the seawater intrusion degree and predict the change of water quality for coastal groundwater resources. This study takes Dagu River in Jiaodong Peninsula of North China as the target area and combines the relevant theoretical research results to build a seawater intrusion fuzzy analytic hierarchy process (AHP) evaluation model. Five sensitive indicators of water quality, such as Cl^-, SO₄^2-, NO₃^-, TH, and TDS, were selected to evaluate the seawater intrusion level of the long series monitoring data in Xilaiwan, Guanzhuang, and Ligezhuang of Dagu River Basin by using the basic fuzzy mathematics principles and the improved hierarchical analysis method. In this study, the cubic exponential smoothing method was applied to predict groundwater quality change in Dagu River Basin. In order to evaluate the change of seawater intrusion in detail and make timely prediction, this paper innovatively divided the classification standard of seawater intrusion degree based on relevant norms and scholars' research and predicted the evaluation level of seawater intrusion by using long series historical observation data combined with fuzzy analytic hierarchy process. The cubic exponential smoothing method which has the characteristics of simple and fast was introduced to fit the observation elements, and the historical data were used to verify the prediction of the future development trend. Compared with the evaluation results of seawater intrusion by traditional methods, this study can reflect the whole development trend of seawater intrusion in detail and has the characteristics of more reasonable, accurate, and practical. It also provides a certain reference for the future seawater intrusion prevention. In addition to this case, the method proposed in this study will be applicable to a wider range of coastal zones, providing a new idea for the rational management and control of coastal groundwater resources.

Development, hotspots and trend directions of groundwater salinization research in both coastal and inland areas: a bibliometric and visualization analysis from 1970 to 2021

As a global concern, the issue of groundwater salinization refers to the phenomenon of an increase in the overall chemical content over background levels in the groundwater. It involves a long-term process that could degrade groundwater quality and restrict its availability for drinking, irrigation and industry. For the effective protection and further research of groundwater resources, policy strongly depends on understanding the development, hotspots and trend directions of groundwater salinization research, which involves the degree, sources and processes of global groundwater salinization. However, such a comprehensive and systematic analysis has not been performed, and it is difficult to have a deeper understanding of groundwater salinization. The purpose of this paper is to analyze the knowledge structure, hot topics and trends in the field of groundwater salinization based on 6651 Web of Science (WoS) publications combined with CiteSpace for in-depth bibliometric and visual analysis. The results showed that 292 institutions in 125 countries have published articles in this field from 1970 to 2021. The USA was one of the most prolific contributors, with the largest number of publications and active institutions. Cooperation among authors has become frequent in recent years, and they tend to cooperate in groups. According to the analysis of co-occurrence keywords and co-cited articles, "water resources", "sea level rise" and "variable density flow" were identified as three hot topics. A keyword burst analysis revealed the emerging trends of concerns about global climate change and the sustainable utilization of water resources. In addition, the possible opportunities and challenges were explored that may be faced in groundwater salinization research. The outcomes of this study are significant for future research on groundwater management and pollution control.

Saltwater intrusion into coastal aquifers in the contiguous United States - A systematic review of investigation approaches and monitoring networks

Saltwater intrusion (SWI) into coastal aquifers is a growing problem for the drinking water supply of coastal communities worldwide, including for the sustainability of coastal ecosystems depending on freshwater inflow. The interface between freshwater and seawater in coastal aquifers is highly dynamic and is sensitive to changes in the hydraulic gradient between the sea- and groundwater levels. Sea level rise, storm surges, and drought are natural drivers changing the hydrostatic equilibrium between fresh- and saltwater. Coastal aquifers are further stressed by groundwater over-pumping because of the increasing needs of coastal populations. A systematic literature review and analysis of the current state of understanding the SWI drivers is presented, focusing on recent (1980 to 2020) investigations in the contiguous United States (CONUS). Results confirm that SWI is an active research area in CONUS. The drivers of SWI are increasingly better understood and quantified; however, the need for increased monitoring is also recognized. Our study shows that the number of monitoring sites have not increased significantly over the review period. Additionally, geophysical, and geochemical investigation techniques and numerical modeling tools are not utilized to their full potential, and data on SWI is not readily available from some sources. We conclude that there is a need for more SWI monitoring networks and closer multi-disciplinary collaboration, particularly between practitioners in the field and emerging modeling technique experts. Though we focus primarily on CONUS, our insights may be of value to the broader SWI research community and coastal water quality managers around the globe.

Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt

To monitor groundwater salinization due to seawater intrusion (SWI) in the aquifer of the eastern Nile Delta, Egypt, we developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) methodologies. Hydrogeological and hydrogeochemical data of the groundwater wells in three periods (1996, 2007, and 2018) were used as input data for the AI methods. All the studied indicators were enrolled in feature extraction process where the most significant inputs were determined, including the studied year, the distance from the shoreline, the aquifer type, and the hydraulic head. These inputs were used to build four basic AI models to get the optimal prediction results of the used indicators (the base exchange index (BEX), the groundwater quality index for seawater intrusion (GQI_SWI), and water quality). The machine learning models utilized in this study are logistic regression, Gaussian process regression, feedforward backpropagation neural networks (FFBPN), and deep learning-based long-short-term memory. The FFBPN model achieved higher evaluation results than other models in terms of root mean square error (RMSE) and R² values in the testing phase, with R² values of 0.9667, 0.9316, and 0.9259 for BEX, GQI_SWI, and water quality, respectively. Accordingly, the FFBPN was used to build a predictive model for electrical conductivity for the years 2020 and 2030. Reasonable results were attained despite the imbalanced nature of the dataset for different times and sample sizes. The results show that the 1000 μS/cm boundary is expected to move inland ~9.5 km (eastern part) to ~10 km (western part) to ~12.4 km (central part) between 2018 and 2030. This encroachment would be hazardous to water resources and agriculture unless action plans are taken.

Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms

A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithms have been adopted to enhance the groundwater contamination vulnerability assessment. The original DRASTIC model (ODM) suffers from the inherited subjectivity and a lack of robustness to assess the final aquifer vulnerability to nitrate contamination. To overcome the drawbacks of the ODM, and to maximize the accuracy of the final contamination vulnerability index, two levels of modeling strategy were proposed. The first modeling strategy used particle swarm optimization (PSO) and differential evolution (DE) algorithms to determine the effective weights of DRASTIC parameters and to produce new indices of ODVI-PSO and ODVI-DE based on the ODM formula. For strategy-2, a deep learning neural networks (DLNN) model used two indices resulting from strategy-1 as the input data. The adjusted vulnerability index in strategy-2 using the DLNN model showed more superior performance compared to the other index models when it was validated for nitrate values. Study results affirmed the capability of the DLNN model in strategy-2 to extract the further information from ODVI-PSO and ODVI-DE indices. This research concluded that strategy-2 provided higher accuracy for modeling the aquifer contamination vulnerability in the study area and established the efficient applicability for the aquifer contamination vulnerability modeling.

Mapping Risk to Land Subsidence: Developing a Two-Level Modeling Strategy by Combining Multi-Criteria Decision-Making and Artificial Intelligence Techniques

Groundwater over-abstraction may cause land subsidence (LS), and the LS mapping suffers the subjectivity associated with expert judgment. The paper seeks to reduce the subjectivity associated with the hazard, vulnerability, and risk mapping by formulating an inclusive multiple modeling (IMM), which combines two common approaches of multi-criteria decision-making (MCDM) at Level 1 and artificial intelligence (AI) at Level 2. Fuzzy catastrophe scheme (FCS) is used as MCDM, and support vector machine (SVM) is employed as AI. The developed methodology is applied in Iran’s Tasuj plain, which has experienced groundwater depletion. The result highlights hotspots within the study area in terms of hazard, vulnerability, and risk. According to the receiver operating characteristic and the area under curve (AUC), significant signals are identified at both levels; however, IMM increases the modeling performance from Level 1 to Level 2, as a result of its multiple modeling capabilities. In addition, the AUC values indicate that LS in the study area is caused by intrinsic vulnerability rather than man-made hazards. Still, the hazard plays the triggering role in the risk realization.

An optimization method coupled the index-overlay method with entropy weighting model to assess seawater intrusion vulnerability

Seawater intrusion poses a serious threat to coastal areas around the world. The purpose of this study was to develop a comprehensive approach to assess the vulnerability of saltwater intrusion. The powerful decision-making technique GALDIT was firstly selected, and its inherent weights are the origin of the subjective method. The entropy method was then integrated to reasonably determine the objective weight of this basic structure. Furthermore, to balance conflicts between subjective and objective methods, game theory was intruded upon. The result of the sensitivity analysis showed a correlation coefficient between the effective weights and theoretical weights of the normal method, entropy theory, and game theory of 0.66, 0.89, and 0.94, respectively. Meanwhile, the best correlation coefficient between the vulnerability indices and the values of 38 monitoring wells was obtained by the game model. Finally, the optimal weights of G, A, L, D, I, and T were 0.096, 0.153, 0.220, 0.320, 0.150, and 0.061, respectively. The study area was finally classified into regions with high, moderate, and low vulnerability, accounting for 11.4%, 24.9%, and 63.7% of the area. The paper included that the optimization of GALDIT through game theory gives a more accurate assessment of the groundwater vulnerability to seawater intrusion.

New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps

Due to excessive exploitation, groundwater resources of coastal regions are exposed to seawater intrusion. Therefore, vulnerability assessments are essential for the quantitative and qualitative management of these resources. The GALDIT model is the most widely used approach for coastal aquifer vulnerability assessment, but suffers from subjectivity of the identification of rates and weights. This study aimes at developing a new hybrid framework for improving the accuracy of coastal aquifer vulnerability assessment using various statistical, metaheuristic, and Multi-Attribute Decision Making (MADM) methods to improve the GALDIT model. The Gharesoo-Gorgan Rood coastal aquifer in northern Iran is used as study site. In order to meet this aim, the Differential Evolution (DE) and Biogeography-Based Optimization (BBO) metaheuristic algorithms were employed to optimize the GALDIT weights. In addition, a novel MADM method, named Step-wise Weight Assessment Ratio Analysis (SWARA), and the bivariate statistical method called statistical index (SI) were used to modify the GALDIT ratings. Finally, correlation coefficients between the maps obtained from each method and Total Dissolved Solid (TDS) as an indicator of seawater intrusion were computed to evaluate the models' prediction power. Correlation coefficients of 0.72, 0.75, 0.76 and 0.78 were obtained for the GALDIT_SWARA-BBO, GALDIT_SI-BBO, GALDIT_SWARA-DE and GALDIT_SI-DE models, respectively. The results from the GALDIT_SI-DE model outperformed all other models at improving the accuracy of the vulnerability assessment. Moreover, the statistical-metaheuristic method yielded more accurate results than SWARA-metaheuristic hybrid models. The vulnerability map of the studied region indicates that the northwestern and western areas are very highly vulnerable. According to GALDIT_SI-DE model, 42%, 17%, 18% and 22% of the aquifer areas respectively have a low, medium, high and very high vulnerability to seawater intrusion. The research findings could be applied by regional authorities to manage and protect groundwater resources.

Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation

Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Shahroud plain in Iran using three different multi-criteria decision making (MCDM) and five different artificial intelligence (AI) models. The MCDM models we used are the VlseKriterijumska Optimizacija IKompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS), and the AI models are the extreme gradient boosting (XGBoost), Cubist, Elasticnet, Bayesian multivariate adaptive regression spline (BMARS) and conditional random forest (Cforest) methods. We used the Receiver Operating Characteristic (ROC) curve, Area Under Curve (AUC) and different statistical indices,i.e. accuracy, sensitivity, specificity, F score, Kappa, Mean Absolute Error (MAE) and Nash-Sutcliffe Criteria (NSC)to validate and evaluate the methods. Based on the different validation techniques, the Cforest method yielded the best results with minimum and maximum values of 0.04 and 0.99, respectively. According to the Cforest model, 30.55% of the study area is extremely vulnerable to land subsidence. The results of our research will be of great help to planners and policy makers in the identification of the most vulnerable regions and the implementation of appropriate development strategies in this area.

SWOT risk analysis towards sustainable aquifer management along the Eastern Mediterranean

This study examines the risks of seawater intrusion (SWI) in data scarce aquifers along the Eastern Mediterranean by quantifying the interaction of the main natural, anthropogenic and climatic drivers, while also considering varying abilities of implementing adaptation and mitigation measures. For this purpose, we conducted a semi-quantitative Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis representing a first attempt at integrating a complex physical process with multi layered influences in a SWOT analysis model that was tested at 26 coastal aquifers with varying levels of SWI severity. The analysis results showed alarming signs of SWI at several eastern and southeastern aquifers, particularly those underlying densely populated centers (i.e. Beirut, Lebanon; Magoza, Cyprus; Gaza, Palestine and the Nile Delta, Egypt). The analysis also highlighted adaptive capabilities that appear to be strong in Cyprus, Israel and Turkey, emerging in Egypt, and weak in Lebanon, Syria, and Palestine. The risks exhibited a strong and statistically significant positive relationship with the reported status of SWI at the tested aquifers thus providing an effective decision-making tool towards the preliminary assessment of SWI in regions with data scarcity. The study concludes with proposing a framework for sustainable aquifer management in the East Med region with emphasis on controlling SWI risks.