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Groundwater Vulnerability, Risk and Hazard: State of the Art Statistical...
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Groundwater Vulnerability, Risk and Hazard: State of the Art Statistical and Machine Learning Techniques

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 7864

Special Issue Editors

Dr. Rahim Barzegar

E-Mail Website
Guest Editor
Department of Bioresource Engineering, McGill University, Montreal, QC, Canada
Interests: hydro(geo)logy; machine/deep learning and data-driven modeling; hydrological forecasting; time series; water quality; groundwater vulnerability and contamination risk; groundwater resources management
Special Issues, Collections and Topics in MDPI journals
Dr. Ata Allah Nadiri

E-Mail Website
Guest Editor
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
Interests: hydrogeology; numerical modelling; artificial intelligence modelling; uncertainty analysis; water contamination; groundwater vulnerability; land subsidence; groundwater drought; risk assessment

Special Issue Information

Dear Colleagues,

Groundwater resources are under natural and anthropogenic pressures that are threatening their sustainability. It is expected to increase those pressures on the groundwater in the future due to increasing agriculture, industry, urbanization, and the impact of climate change and land use which lead to depletion of the quantity and deterioration of the quality of the groundwater. Therefore, groundwater resources should be protected against threatening factors in order to decrease the vulnerability of the groundwaters and/or preventing before degradation.

Evaluation of groundwater vulnerability, risk and hazard are of particular importance for conservation and management purposes. Several methods and frameworks including overlay, statistical, and process-based simulation along with data-driven methods have been used for this purpose in the last decades. This Special Issue calls for high-quality research papers on introducing a novel framework/method to evaluating groundwater quality and quantity vulnerability, risk, and hazard, and application of the statistical and machine learning models to improve the frameworks/methods and other related areas.

Dr. Rahim Barzegar
Dr. Ata Allah Nadiri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • groundwater quality
  • groundwater vulnerability
  • anthropogenic/geogenic contamination
  • land subsidence
  • risk analysis and assessment
  • machine learning
  • deep learning
  • statistical method
  • climate change
  • land use

Published Papers (5 papers)

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Research

19 pages, 5190 KiB  
Article
A Modified GALDIT Method to Assess Groundwater Vulnerability to Salinization—Application to Rhodope Coastal Aquifer (North Greece)
by Despina Chronidou, Evangelos Tziritis, Andreas Panagopoulos, Efstathios K. Oikonomou and Athanasios Loukas
Water 2022, 14(22), 3689; https://0-doi-org.brum.beds.ac.uk/10.3390/w14223689 - 15 Nov 2022
Cited by 1 | Viewed by 1470
Abstract
Aquifer overexploitation in coastal aquifers has led to seawater intrusion that causes severe salinization effects on the groundwater system. The most widespread method for assessing groundwater vulnerability to seawater intrusion, the dominant cause of salinization in coastal aquifers, is the GALDIT method, with [...] Read more.
Aquifer overexploitation in coastal aquifers has led to seawater intrusion that causes severe salinization effects on the groundwater system. The most widespread method for assessing groundwater vulnerability to seawater intrusion, the dominant cause of salinization in coastal aquifers, is the GALDIT method, with numerous applications globally. The present study proposes a modified version of the GALDIT method (GALDIT-Ι) to evaluate the vulnerability of salinization, including its potential additional sources. Both methods have been applied to Rhodope coastal aquifer, an intensively cultivated agricultural area subject to multiple salinization sources. The basic modifications of the proposed GALDIT-I method include different weighting factors and modification of classes for critical parameters, the use of a different indicator (TDS) for the estimation of the Impact factor and, overall, the address of the concept of groundwater salinization instead of seawater intrusion only. The differences in the results of the two methods were significant, as the modified version exhibited a more finite and realistic vulnerability capture, according to the area’s existing hydrogeological and hydrogeochemical knowledge. The original GALDIT method showed an area of nearly 80% as medium vulnerable with very limited spatial deviations. On the other hand, the proposed modified GALDIT method depicted high vulnerability hotspots away from the shoreline, indicating various salinity sources. The validation of the modified method showed that nearly 80% of the sampling points present very good to perfect match between the salinity assessment and the concentration of Cl−, indicating the successful validation of the method. Overall, the GALDIT-I method facilitated groundwater vulnerability assessment to salinization more accurately and exhibited a more discrete spatial assessment, thus, it could be regarded as a promising proactive tool for groundwater management and decision-making. Full article
(This article belongs to the Special Issue Groundwater Vulnerability, Risk and Hazard: State of the Art Statistical and Machine Learning Techniques)
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26 pages, 4570 KiB  
Article
Establishing a Data Fusion Water Resources Risk Map Based on Aggregating Drinking Water Quality and Human Health Risk Indices
by Ata Allah Nadiri, Zahra Sedghi, Rahim Barzegar and Mohammad Reza Nikoo
Water 2022, 14(21), 3390; https://0-doi-org.brum.beds.ac.uk/10.3390/w14213390 - 26 Oct 2022
Cited by 3 | Viewed by 2391
Abstract
The Drinking Water Quality Index (DWQI) and the Human Health Risk Index (HHRI) are two of the most promising tools for assessing the health impact of water quality on humans. Each of these indices has its own ability to determine a specific level [...] Read more.
The Drinking Water Quality Index (DWQI) and the Human Health Risk Index (HHRI) are two of the most promising tools for assessing the health impact of water quality on humans. Each of these indices has its own ability to determine a specific level of safety for drinking, and their results may vary. This study aims to develop an aggregated index to identify vulnerable areas in relation to safe drinking water and, subsequently, risk areas for human health, particularly non-cancerous diseases, in the Maku–Bazargan–Poldasht area in NW Iran through the use of a data fusion technique. Nitrate (NO3−) and fluoride (F−) are the predominant contaminants that threaten the local population’s health. The DWQI revealed that the majority of the study sites had poor to improper quality for drinking water class. Health risk assessments showed an excessive potential for non-carcinogenic health risks because of high NO3− and F− exposure through drinking water. Children are at a higher risk for non-carcinogenic changes than adults, according to the total hazard index (THI; NO3− and F−), suggesting that locals have faced a lifetime risk of non-cancer changes as a consequence of their exposure to these pollutants. Using data fusion techniques can assist in developing a comprehensive water resources risk map for decision-making. Full article
(This article belongs to the Special Issue Groundwater Vulnerability, Risk and Hazard: State of the Art Statistical and Machine Learning Techniques)
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21 pages, 5542 KiB  
Article
Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI
by Ata Allah Nadiri, Rahim Barzegar, Sina Sadeghfam and Ali Asghar Rostami
Water 2022, 14(19), 3185; https://0-doi-org.brum.beds.ac.uk/10.3390/w14193185 - 10 Oct 2022
Cited by 3 | Viewed by 1760
Abstract
The study of groundwater quality is typically conducted using water quality indices such as the Groundwater Quality Index (GQI) or the GroundWater Quality Index (GWQI). The indices are calculated using field data and a scoring system that uses ratios of the constituents to [...] Read more.
The study of groundwater quality is typically conducted using water quality indices such as the Groundwater Quality Index (GQI) or the GroundWater Quality Index (GWQI). The indices are calculated using field data and a scoring system that uses ratios of the constituents to the prescribed standards and weights based on each constituent’s relative importance. The results obtained by this procedure suffer from inherent subjectivity, and consequently may have some conflicts between different water quality indices. An innovative feature drives this research to mitigate the conflicts in the results of GQI and GWQI by using the predictive power of artificial intelligence (AI) models and the integration of multiple water quality indicators into one representative index using the concept of data fusion through the catastrophe theory. This study employed a two-level AI modeling strategy. In Level 1, three indices were calculated: GQI, GWQI, and a data-fusion index based on four pollutants including manganese (Mn), arsenic (As), lead (Pb), and iron (Fe). Further data fusion was applied at Level 2 using supervised learning methods, including Mamdani fuzzy logic (MFL), support vector machine (SVM), artificial neural network (ANN), and random forest (RF), with calculated GQI and GWQI indices at Level 1 as inputs, and data-fused indices target values derived from Level 1 fusion as targets. We applied these methods to the Gulfepe-Zarinabad subbasin in northwest Iran. The results show that all AI models performed reasonably well, and the difference between models was negligible based on the root mean square errors (RMSE), and the coefficient of determination (r2) metrics. RF (r2 = 0.995 and RMSE = 0.006 in the test phase) and MFL (r = 0.921 and RMSE = 0.022 in the test phase) had the best and worst performances, respectively. The results indicate that AI models mitigate the conflicts between GQI and GWQI results. The method presented in this study can also be applied to modeling other aquifers. Full article
(This article belongs to the Special Issue Groundwater Vulnerability, Risk and Hazard: State of the Art Statistical and Machine Learning Techniques)
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13 pages, 2486 KiB  
Article
Risk Assessment of Nitrate Pollution in the Shallow Groundwater of the Mihe Alluvial–Diluvial Fan Based on a DEA Model
by Qi Zhao, Fulin Li, Aihua Zhu, Xiaoming Zhang, Huawei Chen and Tingting Sun
Water 2022, 14(9), 1360; https://0-doi-org.brum.beds.ac.uk/10.3390/w14091360 - 22 Apr 2022
Cited by 2 | Viewed by 1438
Abstract
Affected by excessive fertilizer application and livestock breeding, the problem of nitrate pollution in the groundwater in the Mihe alluvial–diluvial fan area is becoming increasingly prominent, which poses a great threat to human production and life. Given this, the risk of nitrate pollution [...] Read more.
Affected by excessive fertilizer application and livestock breeding, the problem of nitrate pollution in the groundwater in the Mihe alluvial–diluvial fan area is becoming increasingly prominent, which poses a great threat to human production and life. Given this, the risk of nitrate pollution in the shallow groundwater of the Mihe alluvial–diluvial fan is evaluated by introducing a data envelopment analysis (DEA) method. Using this model, 28 groundwater sampling points are selected as the decision-making unit (DMU); the nitrogen and pesticide application rate, livestock and poultry stock, groundwater burial depth, aquifer water abundance, and vegetable planting area are taken as the model input; and the nitrate content is taken as the model output to quantitatively calculate the pollution risk index to form a spatial distribution map of pollution risk. The calculation using the model shows that the average pollution risk index of the study area is 0.382, the spatial variation is 1.12, the pollution risk index gradually decreases from south to north, and agricultural planting and livestock and poultry breeding are the main pollution sources. The calculation of nitrate pollution risk using this model not only enriches the nitrate pollution evaluation model but also provides a basis for further implementing the action of reducing fertilizer use by increasing its efficiency and strengthening the prevention of agricultural diffused pollution. Full article
(This article belongs to the Special Issue Groundwater Vulnerability, Risk and Hazard: State of the Art Statistical and Machine Learning Techniques)
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15 pages, 2777 KiB  
Article
Mapping Risk to Land Subsidence: Developing a Two-Level Modeling Strategy by Combining Multi-Criteria Decision-Making and Artificial Intelligence Techniques
by Ata Allah Nadiri, Marjan Moazamnia, Sina Sadeghfam and Rahim Barzegar
Water 2021, 13(19), 2622; https://0-doi-org.brum.beds.ac.uk/10.3390/w13192622 - 23 Sep 2021
Cited by 11 | Viewed by 1999
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Groundwater Vulnerability, Risk and Hazard: State of the Art Statistical and Machine Learning Techniques)
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