Harnessing deep learning for fusion-based heavy metal contamination index prediction in groundwater.

Groundwater contamination by heavy metals presents a major environmental threat with serious implications for public health and resource sustainability. This study proposes a novel deep learning-based data fusion framework to predict heavy metal contamination index in groundwater, focusing specifically on Manganese (Mn), Iron (Fe), Arsenic (As), and Lead (Pb)-elements found to exceed World Health Organization (WHO) permissible limits in the Gultepe-Zarrinabad sub-basin, Zanjan, Iran. Five widely used water contamination indices (e.

Assessing uncertainties in modeling the risk of geogenic groundwater contamination.

Assessing groundwater contamination risk is a critical aspect of preventing and managing groundwater pollution. There was a research gap in the investigation of uncertainties in modeling groundwater contamination risks in aquifers. This study addresses this gap using Bayesian Model Averaging (BMA), with a novel focus on risk exposures from geogenic contaminants, such as lead (Pb).

Incorporating hydraulic gradient and pumping rate into GALDIT framework to assess groundwater vulnerability to salinity in coastal aquifers: a case study from Urmia Plain, Iran.

The critical role of groundwater in meeting diverse needs, including drinking, industrial, and agricultural, highlights the urgency of effective resource management. Excessive groundwater extraction, especially in coastal regions including Urmia Plain in NW Iran, disrupts the equilibrium between freshwater and saline boundaries within aquifers. Influential parameters governing seawater intrusion-groundwater occurrence (G), aquifer hydraulic conductivity (A), the height of groundwater level above the mean sea level (L), distance from the shore (D), impact of the existing status of seawater intrusion (I), and thickness of the saturated aquifer (T)-merge to shape the GALDIT vulnerability index for coastal aquifers.

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.

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.

Removal of arsenic with functionalized multi-walled carbon nanotubes (MWCNTs-COOH) using the magnetic method (FeO) 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 (FeO) nanoparticles.

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 (NO-N), arsenic (As), boron (B), fluoride (F), and aluminium (Al).

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.

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.

Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts.

A critical understanding of the water crisis of Lake Urmia is the driver in this paper for a basin-wide investigation of its Meteorological (Met) droughts and Groundwater (GW) droughts. The challenge is to formulate a data-driven modelling strategy capable of discerning anthropogenic impacts and resilience patterns through using 21-years of monthly data records. The strategy includes: (i) transforming recorded timeseries into Met/GW indices; (ii) extracting their drought duration and severity; and (iii) deriving return periods of the maximum drought event through the copula method.

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.

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.

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.

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.

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).

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.

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.

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 500km 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.

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.

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.

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.

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.

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.

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.