Groundwater Quality Assessment for Sustainable Drinking and Irrigation

Asadi, Ebrahim; Isazadeh, Mohammad; Samadianfard, Saeed; Ramli, Mohammad Firuz; Mosavi, Amir; Nabipour, Narjes; Shamshirband, Shahaboddin; Hajnal, Éva; Chau, Kwok Wing

doi:10.3390/su12010177

Cited by 127 publications

(60 citation statements)

References 36 publications

(46 reference statements)

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“…Today, with limited water resources, less than one percent of available water resources are suitable for human consumption (1). Therefore, it is essential to protect water resources with proper management.…”

Section: Introductionmentioning

confidence: 99%

“…Uncontrolled discharge of industrial and agricultural wastewater and infiltration of municipal wastewater leads to groundwater contamination (5). Based on the quality of groundwater in different regions, and with proper management, the use of water resources for drinking or agricultural purposes can be allocated (1). The presence of HMs in surface and groundwater is usually related to human industrial activities.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of heavy metals concentration in groundwater and their associated health risks near an industrial area

Lorestani

Merrikhpour²,

Cheraghi

2020

Environ Health Eng Manag

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Background: Heavy metals (HMs) contamination from industrial wastewater is a major environmental problem that has been increasing in the past few years. The purpose of this study was to investigate the current status of HMs contamination in Bu-Ali industrial town, Hamedan, western Iran. Methods: The concentration of 9 serious HMs (arsenic, cadmium, chromium, copper, iron, lead, manganese, nickel, and zinc) in groundwater samples was studied during spring 2017. In order to evaluate water quality for aquaculture and drinking purposes, heavy metal evaluation index (HEI), heavy metal pollution index (HPI), and contamination (Cd) indicator were calculated. Health risk of HMs was also calculated to assess the risk of cancer. Results: The results showed that the mean concentration of the HMs according to the Cd index was as follows: Pb > Ni > Cr > Fe > Cd > As > Cu > Zn > Mn. The mean HEI and HPI values were 89.1 and 815.5, respectively. The results also showed that there was no relationship between the HMs concentration and cancer risk. Conclusion: The concentration of the studied HMs in most samples was higher than the permissible limit for drinking water. The HEI and HPI values in high-risk samples were higher than the permissible limit of drinking water, therefore, there is high risk and limitation for aquatic life, but there is no risk of cancer.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of heavy metals concentration in groundwater and their associated health risks near an industrial area

Lorestani

Merrikhpour²,

Cheraghi

2020

Environ Health Eng Manag

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“…Then, these input combinations were used in data mining methods to estimate evaporation at three stations of Gonbad-e Kavus, Gorgan and Bandar Torkaman. There is no straightforward guideline for splitting the training and testing data in machine learning modeling [38][39][40][41][42][43][44][45][46]. For instance, the study of Choubin [47] used a total of 63% of their data for model development, whereas Qasem et al, [48] utilized 67% of data, Asadi et al, [41], Samadianfard et al, [49,50], and Dodangeh et al, [51] used 70%, and Zounemat-Kermani et al, [52] implemented 80% of total data to develop their models.…”

Section: Resultsmentioning

confidence: 99%

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

et al. 2020

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Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.

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“…The samples from the study area are contaminated due to leakages from underground fuel tanks and septic tanks, seepage through landfills, pesticides used in farms. Asadi et al [11] utilized WQI and Irrigation Water Quality Index used locate the suitable area of water pumping for drinking and agricultural purposes in the Tabriz aquifer, located in East Azerbaijan province, northwest Iran. Collected 39 samples with 12 physical and chemical parameters during 2003 to 2014.…”

Section: Literature Surveymentioning

confidence: 99%

Classification of Groundwater by Applying Simplified Fuzzy Adaptive Resonance Theory

Kiran¹,

Chaparala²,

Radhika³

2021

IJDNE

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Groundwater quality assessment is primarily intended to determine whether the water in a particular area can be used for aquatic purpose or not. The assessment comprises analysis of physical, chemical and microbiological characteristics of groundwater samples. The quality of groundwater can be evaluated through few standard conventional methods viz. Water Quality Index, Canadian Council of Ministries Environmental Water Quality Index and Weighted Arithmetic Water Quality Index etc. In addition to the conventional methods, multivariate statistical methods like Principal Component Analysis, Factor Analysis and Cluster Analysis can also be used to assess the groundwater quality. As these methods are descriptive models, they are inadequate to predict the quality of unknown groundwater sample. Hence, an efficient predictive model is desirable to analyze the characteristic parameters of groundwater samples and predict the quality of an unknown sample. The sample may have both crisp and fuzzy values. Conventional supervised learning methods may not be suitable for constructing the required prediction model as they are not suitable for handling fuzzy input data. Therefore, simplified fuzzy adaptive resonance theory model is an appropriate choice for accomplishing the task of building the prediction model. The present work proposes to assess the quality of groundwater by applying the Weighted Arithmetic Water Quality Index method and Simplified Fuzzy Adaptive Resonance Theory model by considering 7 groundwater quality parameters. The accuracy of afore mentioned approach seems to be pleasing when compared to counter parts like Back propagation and Random Forests classifiers.

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Groundwater Quality Assessment for Sustainable Drinking and Irrigation

Cited by 127 publications

References 36 publications

Assessment of heavy metals concentration in groundwater and their associated health risks near an industrial area

Assessment of heavy metals concentration in groundwater and their associated health risks near an industrial area

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Classification of Groundwater by Applying Simplified Fuzzy Adaptive Resonance Theory

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