Publications by authors named "Asghar Asghari Moghaddam"

10 Publications

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Assessment of groundwater vulnerability using genetic algorithm and random forest methods (case study: Miandoab plain, NW of Iran).

Environ Sci Pollut Res Int 2021 Mar 24. Epub 2021 Mar 24.

Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, East Azerbaijan, Iran.

One of the appropriate ways to prevent groundwater contamination is identifying the vulnerable areas of the aquifers. The DRASTIC framework, for assessing the intrinsic vulnerability of the aquifer, is a common method which uses a specific parameter's weight and a uniform distributed contaminant in overall the aquifer. Therefore, it should be calibrated for specific aquifer and contaminant distribution conditions. In this research, random forest (RF) and genetic algorithm (GA) methods were used for DRASTIC framework optimization in Miandoab plain (NW of Iran). In optimizing the basic DRASTIC framework (BDF) using GA, decision variables are the weight of DRASTIC parameters and weight values for each data layer are the outputs of the optimization. The final optimized map (BDF-GA map) was obtained using overlaying the layers with optimized weights based on the GA method. In optimization of BDF using RF, the model is made up of from 1 to 100 trees and the m parameter or split variables was optimized by changing the number of variables between one and the maximum variables of each subset. Also, the feature selection method is used to reduce the dimensions and increase the accuracy of the model. To induct the nitrate contaminant model, raster layer data of 7 BDF parameters, together with the target variable (VI of BDF map), were used. In the final step, variables' importance was identified by the RF method and then, the vulnerability map was obtained based on variable importance. In validation and comparison of methods with CI and ROC methods, the BDF-RF method with the higher CI and ROC values was ranked as the most accurate approach in groundwater vulnerability evaluation. The optimized map using the RF method (BDF-RF map) showed that 14.5, 13, 18, 26.5, and 28% of the plain are located in areas with very low, low, moderate, high, and very high vulnerability categories, respectively.
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http://dx.doi.org/10.1007/s11356-021-12714-2DOI Listing
March 2021

Modification of the DRASTIC Framework for Mapping Groundwater Vulnerability Zones.

Ground Water 2020 05 12;58(3):441-452. Epub 2019 Jul 12.

Department of Bioresource Engineering, McGill University, 21111 Lakeshore, Ste Anne de Bellevue, Quebec, H9X3V9, Canada.

The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO ) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin-Shahr plain, Iran. Twenty-two groundwater samples were collected from wells in the study area, and their respective NO concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon-GA-DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R of 0.22, the Wilcoxon-GA-DRASTIC obtained a maximum R value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R value to the highest degree.
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http://dx.doi.org/10.1111/gwat.12919DOI Listing
May 2020

Delimitation of groundwater zones under contamination risk using a bagged ensemble of optimized DRASTIC frameworks.

Environ Sci Pollut Res Int 2019 Mar 31;26(8):8325-8339. Epub 2019 Jan 31.

Department of Water Engineering, Faculty of Agriculture, University of Tabriz, 29 Bahman Boulevard, Tabriz, Iran.

Developing a reliable groundwater vulnerability and contamination risk map is very important for groundwater management and protection. This study aims to compare various modified DRASTIC vulnerability frameworks based on rate calibration using the Wilcoxon rank-sum test (WRST), frequency ratio (FR) and weight optimization using the correlation coefficient (CC), the analytic hierarchy process (AHP), and genetic algorithms (GA), as well as to introduce, for the first time, an aggregated approach based on a bagging ensemble to develop a combined modified DRASTIC model. This research was conducted in the Khoy plain, NW Iran. To develop a typical DRASTIC map, seven DRASTIC data layers were generated, weighted, and then overlaid in ArcGIS. The nitrate (NO) concentrations at 54 sites in the study area were used to validate the models by calculating the correlation coefficient (r) between the vulnerability/risk indices and NO concentrations. The calculated r value for the typical DRASTIC was 0.12. A sensitivity analysis reveals that the impact of the vadose zone and conductivity parameters with mean variation indices of 22.2 and 7.5%, respectively, have the highest and lowest influence on aquifer vulnerability. The r values increased for all the optimized frameworks. The results show that the WRST and GA methods are the most effective methods for calibration and optimization of DRASTIC rates and weights, with the WRST-GA-DRASTIC model obtaining an r value of 0.64. A bagging ensemble model was employed to combine the advantages of each standalone model. The bagging ensemble model yields an r value of 0.67. The ensemble model has the potential to increase the r value further than both the standalone optimized frameworks and the typical DRASTIC approach. In terms of spatial distribution class area (%), the bagging ensemble-DRASTIC model demonstrates that the moderate and low contamination risk classes with 16.4 and 23.1% of the total area cover the lowest and highest parts of the plain.
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http://dx.doi.org/10.1007/s11356-019-04252-9DOI Listing
March 2019

Assessing the potential origins and human health risks of trace elements in groundwater: A case study in the Khoy plain, Iran.

Environ Geochem Health 2019 Apr 29;41(2):981-1002. Epub 2018 Sep 29.

Department of Water Engineering, Faculty of Agriculture, University of Tabriz, 29 Bahman Boulevard, Tabriz, Iran.

The objectives of this study were to measure some trace element concentrations in the groundwater of the Khoy area in northwestern Iran, understand their potential origins using multivariate statistical approaches (correlation analysis, cluster analysis and factor analysis), and evaluate their non-carcinogenic human health risks to local residents through drinking water intake. The trace element status of the groundwater and the associated health risks in the study area have not previously been reported. Groundwater water samples were collected from 54 water sources in July 2017 in the study area. Samples were measured for EC, pH, major and minor elements and some trace elements (Fe, Mn, Al, Zn, Cr, Pb, Cd, Co, Ni and As). The levels of EC, F, Cd, Pb, Zn, As and all the major ions except K exceeded permissible levels for drinking water. Multivariate analysis showed that the quality of groundwater was mainly controlled by geogenic factors followed by anthropogenic impacts. Health risk assessment results indicated that Cr and As in the groundwater, with hazard quotient values of 0.0001 and 11.55, respectively, had the lowest and highest impacts of non-carcinogenic risk to adults and children in the area. The high-risk samples were mainly situated in the northeast and southwest of the Khoy plain where the groundwater was saline. The health risk associated with water consumption from the unconfined aquifer was higher than that from the confined aquifer in the study area. Special attention should be paid to groundwater management in the high-risk areas to control factors (e.g., EC, pH and redox) that stimulate the release of trace elements into groundwater.
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http://dx.doi.org/10.1007/s10653-018-0194-9DOI Listing
April 2019

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

Sci Total Environ 2018 Apr 30;621:697-712. Epub 2017 Nov 30.

Hellenic Agricultural Organization, Soil and Water Resources Institute, 57400 Sindos, Greece.

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model.
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http://dx.doi.org/10.1016/j.scitotenv.2017.11.185DOI Listing
April 2018

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

Sci Total Environ 2018 Feb 26;613-614:693-706. Epub 2017 Sep 26.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran. Electronic address:

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.
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http://dx.doi.org/10.1016/j.scitotenv.2017.08.027DOI Listing
February 2018

Hydrogeochemistry and water quality of the Kordkandi-Duzduzan plain, NW Iran: application of multivariate statistical analysis and PoS index.

Environ Monit Assess 2017 Aug 18;189(9):455. Epub 2017 Aug 18.

Hellenic Agricultural Organization, Soil and Water Resources Institute, 57400, Sindos, Greece.

Kordkandi-Duzduzan plain is one of the fertile plains of East Azarbaijan Province, NW of Iran. Groundwater is an important resource for drinking and agricultural purposes due to the lack of surface water resources in the region. The main objectives of the present study are to identify the hydrogeochemical processes and the potential sources of major, minor, and trace metals and metalloids such as Cr, Mn, Cd, Fe, Al, and As by using joint hydrogeochemical techniques and multivariate statistical analysis and to evaluate groundwater quality deterioration with the use of PoS environmental index. To achieve these objectives, 23 groundwater samples were collected in September 2015. Piper diagram shows that the mixed Ca-Mg-Cl is the dominant groundwater type, and some of the samples have Ca-HCO, Ca-Cl, and Na-Cl types. Multivariate statistical analyses indicate that weathering and dissolution of different rocks and minerals, e.g., silicates, gypsum, and halite, ion exchange, and agricultural activities influence the hydrogeochemistry of the study area. The cluster analysis divides the samples into two distinct clusters which are completely different in EC (and its dependent variables such as Na, K, Ca, Mg, SO, and Cl), Cd, and Cr variables according to the ANOVA statistical test. Based on the median values, the concentrations of pH, NO, SiO, and As in cluster 1 are elevated compared with those of cluster 2, while their maximum values occur in cluster 2. According to the PoS index, the dominant parameter that controls quality deterioration is As, with 60% of contribution. Samples of lowest PoS values are located in the southern and northern parts (recharge area) while samples of the highest values are located in the discharge area and the eastern part.
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http://dx.doi.org/10.1007/s10661-017-6171-4DOI Listing
August 2017

Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models.

Sci Total Environ 2017 Dec 29;599-600:20-31. Epub 2017 Apr 29.

Hellenic Agricultural Organization, Soil and Water Resources Institute, 57400 Sindos, Greece.

Accurate prediction of groundwater level (GWL) fluctuations can play an important role in water resources management. The aims of the research are to evaluate the performance of different hybrid wavelet-group method of data handling (WA-GMDH) and wavelet-extreme learning machine (WA-ELM) models and to combine different wavelet based models for forecasting the GWL for one, two and three months step-ahead in the Maragheh-Bonab plain, NW Iran, as a case study. The research used totally 367 monthly GWLs (m) datasets (Sep 1985-Mar 2016) which were split into two subsets; the first 312 datasets (85% of total) were used for model development (training) and the remaining 55 ones (15% of total) for model evaluation (testing). The stepwise selection was used to select appropriate lag times as the inputs of the proposed models. The performance criteria such as coefficient of determination (R), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSC) were used for assessing the efficiency of the models. The results indicated that the ELM models outperformed GMDH models. To construct the hybrid wavelet based models, the inputs and outputs were decomposed into sub-time series employing different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Dmeyer of different orders at level two. Subsequently, these sub-time series were served in the GMDH and ELM models as an input dataset to forecast the multi-step-ahead GWL. The wavelet based models improved the performances of GMDH and ELM models for multi-step-ahead GWL forecasting. To combine the advantages of different wavelets, a least squares boosting (LSBoost) algorithm was applied. The use of the boosting multi-WA-neural network models provided the best performances for GWL forecasts in comparison with single WA-neural network-based models.
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http://dx.doi.org/10.1016/j.scitotenv.2017.04.189DOI Listing
December 2017

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

Environ Sci Pollut Res Int 2017 Mar 13;24(9):8562-8577. Epub 2017 Feb 13.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.

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.
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http://dx.doi.org/10.1007/s11356-017-8489-4DOI Listing
March 2017

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

Sci Total Environ 2017 Jan 14;574:691-706. Epub 2016 Oct 14.

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azarbaijan, Iran. Electronic address:

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.
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http://dx.doi.org/10.1016/j.scitotenv.2016.09.093DOI Listing
January 2017