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.

A geospatial approach for assessing urban flood risk zones in Chennai, Tamil Nadu, India

Chennai, the capital city of Tamil Nadu in India, has experienced several instances of severe flooding over the past two decades, primarily attributed to persistent heavy rainfall. Accurate mapping of flood-prone regions in the basin is crucial for the comprehensive flood risk management. This study used the GIS-MCDA model, a multi-criteria decision analysis (MCDA) model that incorporated geographic information system (GIS) technology to support decision making processes. Remote sensing, GIS, and analytical hierarchy technique (AHP) were used to identify flood-prone zones and to determine the weights of various factors affecting flood risk, such as rainfall, distance to river, elevation, slope, land use/land cover, drainage density, soil type, and lithology. Four groups (zones) were identified by the flood susceptibility map including high, medium, low, and very low. These zones occupied 16.41%, 67.33%, 16.18%, and 0.08% of the area, respectively. Historical flood events in the study area coincided with the flood risk classification and flood vulnerability map. Regions situated close to rivers, characterized by low elevation, slope, and high runoff density were found to be more susceptible to flooding. The flood susceptibility map generated by the GIS-MCDA accurately described the flood-prone regions in the study area.

Removal of arsenic with functionalized multi-walled carbon nanotubes (MWCNTs-COOH) using the magnetic method (Fe3O4) from aqueous solutions

Heavy metals such as arsenic are one of the most important water pollutants and cause many environmental problems. One of the mechanisms for removing arsenic from aqueous media is the adsorption process. In this study, we investigated the efficiency of magnetized multi-walled carbon nanotubes with iron oxide (Fe3O4) nanoparticles. The precipitation method was used to synthesize Fe3O4 on PAC-(Fe3O4-f/MWCNTs) functionalized multi-walled carbon nanotubes. The effects of pH, contact time, amount of adsorbent, and contaminant concentration on the adsorption process were examined. Residual arsenic concentration was measured using induction chromatography and inductively coupled plasma mass spectrometry (ICP-MS). The physical and structural characteristics of the adsorbent were analyzed using XRD, TEM, FT-IR, TGA-DTA, BET, FESEM-EDS, Raman spectrum and X-ray. Optimal conditions for arsenic removal were pH = 2, As concentration = 6 mg L-1, and contact time = 30 minutes, using 0.02 g of adsorbent at room temperature. Also, fitting regression curves to the results showed that the Freundlich model (R2 > 0.9981) and a pseudo-second-order model (R2 = 1) best describe the isothermal and kinetic models of the adsorption process, respectively.

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).

Efficacy of hydrotherapy, spa therapy, and balneotherapy for psoriasis and atopic dermatitis: a systematic review

BACKGROUND

Atopic dermatitis (AD) and psoriasis are chronic inflammatory diseases that have significant skin complications.

OBJECTIVE

The purpose of this systematic study was to evaluate the evidence obtained from human studies on the effects of hydrotherapy, spa therapy, and balneotherapy in psoriasis and atopic dermatitis.

METHODS

The present systematic review was conducted according to the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statements. Also, for this study databases such as Embase, PubMed, Scopus ProQuest, and sciences direct database were searched from the beginning to April 2021.

RESULTS

All human studies that examined the effect of balneotherapy, spa therapy, and hydrotherapy on psoriasis and atopic dermatitis were published in the form of a full article in English. In the end, only 22 of the 424 articles met the criteria for analysis. Most studies have shown that balneotherapy, spa therapy, and hydrotherapy may reduce the effects of the disease by reducing inflammation and improving living conditions. In addition, the results of the Downs and Black score show that seven studies received very good scores, three studies received good scores, nine studies received fair scores, and three studies received poor scores.

CONCLUSIONS

The results of studies also showed that hydrotherapy leads to an improvement in the PASI score index. Nevertheless, more clinical trials are needed to determine the mechanism of action of hydrotherapy on these diseases.

Using a soft computing OSPRC risk framework to analyze multiple contaminants from multiple sources; a case study from Khoy Plain, NW Iran

Water shortage in arid and semi-arid areas like Iran makes groundwater contamination a crucial issue. In the Khoy aquifer, NW Iran, contaminants (e.g., arsenic (As), nitrate (NO₃^-), lead (Pb), and zinc (Zn)) may originate from both geological and anthropogenic sources. The objectives of the study are to (1) employ soft modeling framework to abstract available hydrogeochemical information into a perceptual model and (2) build a conceptual model using the risk cells (RCs) by applying the following two steps: (i) study Origin-Source-Pathways-Receptor-Consequence (OSPRC) as a risk system; and (ii) apply "soft modeling" as a set of diverse and classical tools including graphical representations, geological surveys, and multivariate statistical analysis to validate the information by evaluating their convergence or divergence behaviors among different tools used for investigating the groundwater contaminants. According to the perceptual model, the Khoy aquifer contains four RCs. RC4 (southern of plain) and RC2 (northern of the plain) contain high levels of As, while RC2 contains high amounts of Zn. In RC1 (northern of plain) and RC3 (middle of plain), a high concentration of Pb is detected, while in RC3 and RC4, there is a high concentration of NO₃^-. It was found that a soft modeling approach can only identify the dominant hydrogeochemical processes for each RC as a descriptive model, rather than the use of quantitative models if sufficient data are available.

Inferences on metal pollution in the natural spawning zone of Bangladesh river and pollution management strategies

The purpose of this study was to evaluate the metal concentrations in the Halda River in Bangladesh to determine the quality of the water and sediment in the natural spawning zone. Fe > Zn > Cr > Cd > Cu was the order of the metals in water, whereas Fe > Zn > Cd > Cu was the order in sediments. Almost all of the heavy metals in the water and sediment had been found within the established limits, with the exception of Cr and Fe in the river and Cu in the sediment. In the case of water, Cr vs. Zn was found to have the strongest correlation (r = 0.96). Due to the coagulation and adsorption processes, it was shown that Fe and Zn had a substantial correlation of 0.96, Cu and Cd of 0.91, and Cr of 0.78 with Zn. Hazard quotient values of Cd show the not potable nature of Halda river surface water and might give adverse health effects for all age groups except Cu and Zn. Pollution load index values indicated the uncontaminated nature of the river bottom sediments. Natural and human activities were the key factors influencing the accumulation and movement of heavy metals in the water and sediments. Contamination sources are industrial effluents, garbage runoff, farming operations, and oil spills from fishing vessels which are comparable according to multivariate statistical analysis. Ion exchange, absorption, precipitation, complexation, filtration, bio-absorption, redox reaction, and reverse osmosis were considered to be effective for the degradation of metal concentrations. The feasibility of the suggested metal reduction procedures has to be studied to know which is optimally appropriate for this river region. It is expected that this study could provide a useful suggestion to decrease the metal pollution in the river.

Formulating Convolutional Neural Network for mapping total aquifer vulnerability to pollution

Aquifer vulnerability mapping to pollution is topical research activity, and common frameworks such as the basic DRASTIC framework (BDF) suffer from the inherent subjectivity. This paper formulates an artificial intelligence modeling strategy based on Convolutional Neural Network (CNN) to decrease subjectivity. This formulation considers three definitions of intrinsic, specific, and total vulnerabilities. Accordingly, three CNN models are trained and tested to calculate IVI, SVI, and TVI, respectively referring to the intrinsic, specific, and total vulnerability indices. The formulation is applied in an unconfined aquifer northwest of Iran and delineates hotspots within the aquifer. The area under curve (AUC) values derived by the receiver operating curves evaluate the vulnerability indices versus nitrate concentrations. The AUC values for BDF, IVI, SVI, and TVI are 0.81, 0.91, 0.95, and 0.95, respectively. Therefore, CNNs significantly improve the results compared to BDF, but IVI, SVI, and TVI have approximately identical performances. However, the visual comparison between their results provides evidence that significant differences exist between the spatial patterns despite identical AUC values.

A study of uncertainties in groundwater vulnerability modelling using Bayesian model averaging (BMA)

Bayesian Model Averaging (BMA) is used to study inherent uncertainties using the Basic DRASTIC Framework (BDF) for assessing the groundwater vulnerability in a study area related to Lake Urmia. BMA is naturally an Inclusive Multiple Modelling (IMM) strategy at two levels; at Level 1 multiple models are constructed and the paper constructs three AI (Artificial Intelligence) models, which comprise ANN (Artificial Neural Network), GEP (Gene Expression Programming), and SVM (Support Vector Machines) but their outputs are fed to the next level model; at Level 2, BMA combines ANN, GEP and SVM (the Level 1 models) to quantify their inherent uncertainty in terms of within and in-between model errors. The model performance is tested by using the nitrate-N concentrations measured for the aquifer. The results show that in this particular study area, Level 1 models, even BDF, are quite accurate, but the above modelling strategy maximises the extracted information from the local data and BMA reveals that the higher uncertainties occur at areas with higher vulnerability; whereas lower uncertainties are observed at areas with lower vulnerabilities.

An investigation into time-variant subsidence potentials using inclusive multiple modelling strategies

Groundwater over-abstraction due to the absence of an effective management plan is often one of the main reasons for land subsidence in aquifer areas. This paper investigates this environmental problem at Salmas plain, Iran, by using the ALPRIFT framework, an acronym of a set of seven general-purpose data layers, introduced recently by the authors. It is capable of mapping Subsidence Vulnerability Indices (SVI) and the paper investigates an innovation to transform it into Time-variant SVI (TSVI) mapping capabilities through a three module strategy: Module 1: maps SVI; Module 2: develops a predictive model for Groundwater Levels (GWL); Module 3: combines both modules to produces TSVI maps. Modules 1 and 2 employ Inclusive Multiple Modelling (IMM) practices, which promote learning from multiple models, as opposed to their ranking and selecting a 'superior' one. IMM is implemented through the same single modelling strategy for both Modules 1 and 2 at two levels: at Level 1, multiple models are constructed by three Fuzzy Logic (FL) models: Sugeno FL (SFL), Mamdani FL (MFL) and Larsen FL (LFL). (ii) At Level 2, FL models at Level 1 are reused by Support Vector Machine (SVM) as the combiner model. The results show that (i) the models at Level 1 are fit-for-purpose; (ii) the models at Level 2 are defensible owing to IMM strategies focussed on enhancing their accuracy and investigating their residuals; and (iii) according to TSVI maps, the north of the plain is vulnerable to hotspot areas and is exposed to subsidence risks due to unplanned over-abstraction of groundwater from the aquifer at Salmas plain.

An investigation to human health risks from multiple contaminants and multiple origins by introducing 'Total Information Management'

A capability for aggregating risks to aquifers is explored in this paper for cases with sparse data exposed to anthropogenic and geogenic contaminants driven by poor/non-existent planning/regulation practices. The capability seeks 'Total Information Management' (TIM) under sparse data by studying hydrogeochemical processes, which is in contrast to Human Health Risk Assessment (HHRA) by the USEPA for using sample data and a procedure with prescribed parameters without deriving their values from site data. The methodology for TIM pools together the following five dimensions: (i) a perceptual model to collect existing knowledge-base; (ii) a conceptual model to analyse a sample of ion-concentrations to determine groundwater type, origin, and dominant processes (e.g. statistical, graphical, multivariate analysis and geological survey); (iii) risk cells to contextualise contaminants, where the paper considers nitrate, arsenic, iron and lead occurring more than three times their permissible values; (iv) 'soft modelling' to firm up information by learning from convergences and/or divergences within the conceptual model; and (v) study the processes within each risk cell through the OSPRC framework (Origins, Sources, Pathways, Receptors and Consequence). The study area comprises a series of patchy aquifers but HHRA ignores such contextual data and provides some evidence on both carcinogenic and non-carcinogenic risks to human health. The TIM capability provides a greater insight for the processes to unacceptable risks from minor ions of anthropogenic nitrate pollutions and from trace ions of arsenic, iron and lead contaminants.

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

Unplanned groundwater exploitation in coastal aquifers results in water decline and consequently triggers saltwater intrusion (SWI). This study formulates a novel modeling strategy based on GALDIT method using Artificial Intelligence (AI) models for mapping the vulnerability to SWI. This AI-based modeling strategy is a two-level learning process, where vulnerability to SWI at Level 1 can be predicted by such models as Artificial Neural Network (ANN), Sugeno Fuzzy Logic (SFL), and Neuro-Fuzzy (NF); and their outputs serve as the input to the model at Level 2, such as Support Vector Machine (SVM). This model is applied to Urmia aquifer, west coast of Lake Urmia, where both are currently declining. The construction of the above four models both at Levels 1 and 2 provide tools for mapping the SWI vulnerability of the study area. Model performances in the paper are studied using RMSE and R² metrics, where the models at Level 1 are found to be fit-for-purpose and the SVM at Level 2 is improved particularly with respect to the reduced scale of scatters in the results. Evaluating the result and groundwater samples by Piper diagram confirms the correspondence of SWI status with vulnerability index.

Sequential treatment of paper and pulp industrial wastewater: Prediction of water quality parameters by Mamdani Fuzzy Logic model and phytotoxicity assessment

Recycling of industrial wastewater meeting quality standards for agricultural and industrial demands is a viable option. In this study, paper and pulp industrial wastewater were treated with three biological treatments viz. aerobic, anaerobic and sequential (i.e. 20 days of anaerobic followed by 20 days of aerobic cycle), associated with simulation modeling by Mamdani Fuzzy Logic (MFL) model of some selected parameters. Electric air diffuser and minimal salt medium in sealed plastic bottles at control temperature were used for aerobic and anaerobic treatments, respectively. The significant reduction in chemical (COD: 81%) and biological oxygen demand (BOD: 71%), total suspended (TSS: 65%), dissolved solids (TDS: 60%) and turbidity (68%) was recorded during sequential treatment. The treated water was irrigated to determine its phytotoxic effects on seed germination, vigor and seedling growth of mustard (Brassica campestris). Sequential treatment greatly reduced phytotoxicity of wastewater and showed the highest germination percentage (90%) compared to aerobic (60%), anaerobic (70%) treatments and untreated wastewater (30%). Regression analysis also endorsed these findings (R² = 0.76-0.95 between seed germination, seedling growth and vigor). MFL technique was adopted to simulate sequential treatment process. The results support higher performance of MFL model to predict TDS, TSS, COD, and BOD based on the physico-chemical water quality parameters of raw wastewater, time of treatment and treatment type variation. Based on these findings, we conclude that the sequential treatment could be a more effective strategy for treatment of pulp and paper industrial wastewater with efficiency to be used for agricultural industry without toxic effects.

Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks

An investigation is presented to improve on the performances of the Basic DRASTIC Framework (BDF) and its variation by the Fuzzy-Catastrophe Framework (FCF), both of which provide an estimate of intrinsic aquifer vulnerabilities to anthropogenic contamination. BDF prescribes rates and weights for 7 data layers but FCF is an unsupervised learning framework based on a multicriteria decision theory by integrating fuzzy membership function and catastrophe theory. The challenges in the paper include: (i) the study area comprises confined and unconfined aquifers and (ii) Artificial Intelligence (AI) is used to run Multiple Framework (AIMF) in order to map specific vulnerability due to a specific contaminant. Predicted results by AIMF are referred to as Specific Vulnerability Indices, as the learned VIs are referenced to site-specific nitrate-N. The results show that correlation coefficient between BDF or FCF with nitrate-N is lower than 0.7 but the AIMF strategy improves it to greater than 0.95. The results are evidence for the proof-of-concept for transforming frameworks to models capable of further learning.

Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT

Proof-of-concept (PoC) is presented for a new framework to serve as a proactive capability to mapping subsidence vulnerability of Shabestar plain of approximately 500km² overlaying an important aquifer supporting a region renowned for diversity of agricultural products. This aquifer is one of 12 in East and West Azerbaijan provinces, Northwest Iran, which surround the distressed Lake Urmia, with its water table declined approximately 4m in between 2004 and 2014. The decline of water table in aquifers undermines their soil texture and structure by exposure to pressures under their weight and thereby induce or trigger land subsidence. Inspired by the DRASTIC framework to map intrinsic aquifer vulnerability to anthropogenic pollution, the paper introduces the ALPRIFT framework for subsidence, which comprises the seven data layers of Aquifer media (A), Land use (L), Pumping of groundwater, Recharge (R), aquifer thickness Impact (I), Fault distance (F) and decline of water Table (T). The paper prescribes rates to account for local variations and weights for the relative importance of the data layers. The proof-of-concept for ALPRIFT is supported by the correlation of Subsidence Vulnerability Indices (SVIs) with measured subsidence values, which renders a value of 0.5 but improves significantly to 0.86 when using fuzzy logic. Similar improvements are suggested by the ROC/AUC performance metric.

Introducing the risk aggregation problem to aquifers exposed to impacts of anthropogenic and geogenic origins on a modular basis using 'risk cells'

Proof-of-concept is presented in this paper to a methodology formulated for indexing risks to groundwater aquifers exposed to impacts of diffuse contaminations from anthropogenic and geogenic origins. The methodology is for mapping/indexing, which refers to relative values but not their absolute values. The innovations include: (i) making use of the Origins-Source-Pathways-Receptors-Consequences (OSPRC) framework; and (ii) dividing a study area into modular Risk (OSPRC) Cells to capture their idiosyncrasies with different origins. Field measurements are often sparse and comprise pollutants and water table, which are often costly; whereas supplementary data are general-purpose data, which are widely available. Risk mapping for each OSPRC cell is processed by dividing a study area into pixels and for each pixel, the risk from both anthropogenic and geogenic origins are indexed by using algorithms related to: (i) Vulnerability Indices (VI), which identify the potential for risk exposures at each pixel; and (ii) velocity gradient, which expresses the potency to risk exposures across the risk cell. The paper uses DRASTIC for anthropogenic VI but introduces a new framework for geogenic VI. The methodology has a generic architecture and is flexible to modularise risks involving any idiosyncrasies in a generic way in any site exposed to environmental pollution risks. Its application to a real study area provides evidence for the proof-of-concept for the methodology by a set of results that are fit-for-purpose and provides an insight into the study area together with the identification of its hotspots.

The problem of identifying arsenic anomalies in the basin of Sahand dam through risk-based 'soft modelling'

An investigation is undertaken to identify arsenic anomalies at the complex of Sahand dam, East Azerbaijan, northwest Iran. The complex acts as a system, in which the impounding reservoir catalyses system components related to Origin-Source-Pathways-Receptor-Consequence (OSPRC) viewed as a risk system. This 'conceptual framework' overlays a 'perceptual model' of the physical system, in which arsenic with geogenic origins diffused into the formations through extensive fractures swept through the region during the Miocene era. Impacts of arsenic anomalies were local until the provision of the impounding reservoir in the last 10years, which transformed it into active system-wide risk exposures. The paper uses existing technique of: statistical, graphical, multivariate analysis, geological survey and isotopic study, but these often seem ad hoc and without common knowledgebase. Risk analysis approaches are sought to treat existing fragmentation in practices of identifying and mitigating arsenic anomalies. The paper contributes towards next generation best practice through: (i) transferring and extending knowledge on the OSPRC framework; (ii) introducing 'OSPRC cells' to capture unique idiosyncrasies at each cell; and (iii) suggesting a 'soft modelling' procedure based on assembling knowledgebase of existing techniques with partially converging and partially diverging information levels, where knowledgebase invokes model equations with increasing resolutions. The data samples from the study area for the period of 2002-12 supports the study and indicates the following 'risk cells' for the study area: (i) local arsenic risk exposures at south of the reservoir, (ii) system-wide arsenic risks at its north; and (iii) system-wide arsenic risk exposures within the reservoir even after dilution.

Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures

Driven by contamination risks, mapping Vulnerability Indices (VI) of multiple aquifers (both unconfined and confined) is investigated by integrating the basic DRASTIC framework with multiple models overarched by Artificial Neural Networks (ANN). The DRASTIC framework is a proactive tool to assess VI values using the data from the hydrosphere, lithosphere and anthroposphere. However, a research case arises for the application of multiple models on the ground of poor determination coefficients between the VI values and non-point anthropogenic contaminants. The paper formulates SCFL models, which are derived from the multiple model philosophy of Supervised Committee (SC) machines and Fuzzy Logic (FL) and hence SCFL as their integration. The Fuzzy Logic-based (FL) models include: Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Larsen Fuzzy Logic (LFL) models. The basic DRASTIC framework uses prescribed rating and weighting values based on expert judgment but the four FL-based models (SFL, MFL, LFL and SCFL) derive their values as per internal strategy within these models. The paper reports that FL and multiple models improve considerably on the correlation between the modeled vulnerability indices and observed nitrate-N values and as such it provides evidence that the SCFL multiple models can be an alternative to the basic framework even for multiple aquifers. The study area with multiple aquifers is in Varzeqan plain, East Azerbaijan, northwest Iran.

Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models

Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.

Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM)

This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters.

Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran

BACKGROUND

Extensive human activities and unplanned land uses have put groundwater resources of Shiraz plain at a high risk of nitrate pollution, causing several environmental and human health issues. To address these issues, water resources managers utilize groundwater vulnerability assessment and determination of protection. This study aimed to prepare the vulnerability maps of Shiraz aquifer by using Composite DRASTIC index, Nitrate Vulnerability index, and artificial neural network and also to compare their efficiency.

METHODS

The parameters of the indexes that were employed in this study are: depth to water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, hydraulic conductivity, and land use. These parameters were rated, weighted, and integrated using GIS, and then, used to develop the risk maps of Shiraz aquifer.

RESULTS

The results indicated that the southeastern part of the aquifer was at the highest potential risk. Given the distribution of groundwater nitrate concentrations from the wells in the underlying aquifer, the artificial neural network model offered greater accuracy compared to the other two indexes. The study concluded that the artificial neural network model is an effective model to improve the DRASTIC index and provides a confident estimate of the pollution risk.

CONCLUSIONS

As intensive agricultural activities are the dominant land use and water table is shallow in the vulnerable zones, optimized irrigation techniques and a lower rate of fertilizers are suggested. The findings of our study could be used as a scientific basis in future for sustainable groundwater management in Shiraz plain.