Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam, Algeria

Hamil, Somia; Arab, Siham; Chaffai, Amin; Baha, Mounia; Arab, Abdeslem

doi:10.1007/s12517-018-4102-5

Cited by 34 publications

(15 citation statements)

References 29 publications

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“…Moreover, risk assessment index (RAI) and cancer risk index (CRI) [20][21][22] was evaluated to predict the possibilities of carcinogenic impact on human due to direct drinking of river water. Furthermore, environmetrics which involves multivariate statistical methods like principal component analysis (PCA) and hierarchical cluster analysis (HCA) [23,24] was used to develop a composite indicator from the entire heavy metal datasets, and to identify the probable sources that significantly affect river WQ in the area under study.…”

Section: Introductionmentioning

confidence: 99%

Estimation of physicochemical characteristics and associated metal contamination risk in the Narmada River, India

Mishra

Kumar

2020

Environmental Engineering Research

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River Narmada is the fifth largest holy river of Madhya Pradesh (M.P) flowing in the central part of India. The river receives large quantity of untreated/partially treated wastewater enriched with heavy metals and supplementary toxic organic pollutants. This study aims to assess the water quality status in river Narmada using indices like comprehensive pollution index (CPI), heavy metal pollution index (HPI), risk assessment index (RAI) and cancer risk index (CRI), for human use. The presence of faecal coliform and high chemical oxygen demand > 20 mg/L indicates that the water is unsuitable for drinking purposes. The average CPI and HPI evaluated to be 1.98, and 1.35, respectively signifies the moderately polluted river water. Considerably, the RAI > 1 and CRI > 1 is obtained at all sampling locations that alarms the possible cancer risk to human if untreated river water is used. Principal component analysis of data confirmed pollution in the river from both natural and anthropogenic sources. The strongest Pearson correlation coefficient between Cu-Pb (0.998) and Zn-Cu (0.986) indicates the input of wastewater in the river probably from electroplating industries. The river water is unsuitable for human intake. It is required to control direct flow of wastewater in river to restore ecological health.

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Section: Introductionmentioning

confidence: 99%

Estimation of physicochemical characteristics and associated metal contamination risk in the Narmada River, India

Mishra

Kumar

2020

Environmental Engineering Research

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“…where, Y is the dependent variable, X 1 …X m are several independent variables, β0…β m are regression coe cients, and ε is the random error (Hamil et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

An Endeavor of ‘Deep-underground Agriculture’: Deep-underground Storage in a Gold Mine Impacts the Germination of Canola (Brassica Napus L.) Seeds

Wang

et al. 2021

Preprint

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Exploring and utilizing the agronomic potential of deep-underground is one of the ways to cope with the challenges of sudden environmental change on agriculture. Understanding the effects of environmental stresses on the morphological and physiological indicators of crop seeds after their storage deep-underground is crucial to developing and implementing strategies for agriculture in the deep-underground space. In this study, we stored canola seeds in envelopes or sealed packages at 0, 240, 690, and 1,410 m in a gold mine. Seeds in envelopes were retrieved at 42, 66, 90, and 227 days of storage, whereas seeds in sealed packages were retrieved at 66 and 227 days of storage. The germination tests were conducted to investigate the effects of storage depth, duration, and packing method on stored and non-stored seeds. Results showed that increased depth and duration led to decreased seed germination rate, with the germination and vigor indexes also descending to varying degrees. Increased hypocotyl length and biomass accumulation suggested that deep-underground environment had more significant compensatory effect on seed germination. For all indicators, the performance of seeds sealed in packages was superior to those stored in envelopes. Regression analysis showed that it was difficult to obtain the optimal value of each indicator simultaneously. The successful germination experiment foreshadowed the possibilities of deep-underground agriculture in the future.

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“…Traditional (deterministic and stochastic) models, such as statistical approaches and visual modelling, have been commonly used in literature (Sun and Gui 2015;Tziritis and Lombardo 2017;Chen et al 2018;Karami et al 2018). Statistical-based water quality models, such as cluster analysis (CA), hierarchical cluster analysis (HCA) and principal component analysis (PCA), have been commonly used to classify and evaluate correlations between water constituents or parameters (Liu et al 2011;Gu et al 2016;Hamil et al 2018;Lu and Ma 2020). However, data requirements for these approaches are enormous, difficult, time-consuming and expensive to obtain.…”

Section: Introductionmentioning

confidence: 99%

Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches

2021

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Monitoring of water quality through accurate predictions provides adequate information about water management. In the present study, three different modelling approaches: Gaussian process regression (GPR), backpropagation neural network (BPNN) and principal component regression (PCR) models were used to predict the total dissolved solids (TDS) as water quality indicator for the water quality management. The performance of each model was evaluated based on three different sets of inputs from groundwater (GW), surface water (SW) and drinking water (DW). The GPR, BPNN and PCR models used in this study gave an accurate prediction of the observed data (TDS) in GW, SW and DW, with the R2 consistently greater than 0.850. The GPR model gave a better prediction of TDS concentration, with an average R2, MAE and RMSE of 0.987, 4.090 and 7.910, respectively. For the BPNN, an average R2, MAE and RMSE of 0.913, 9.720 and 19.137, respectively, were achieved, while the PCR gave an average R2, MAE and RMSE of 0.888, 11.327 and 25.032, respectively. The performance of each model was assessed using efficiency based indicators such as the Nash and Sutcliffe coefficient of efficiency (ENS) and the index of agreement (d). The GPR, BPNN and PCR models, respectively, gave an ENS of (0.967, 0.915, 0.874) and d of (0.992, 0.977, 0.965). It is understood from this study that advanced machine learning approaches (e.g. GPR and BPNN) are appropriate for the prediction of water quality indices and would be useful for future prediction and management of water quality parameters of various water supply systems in mining communities where artificial intelligence technology is yet to be fully explored.

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Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam, Algeria

Cited by 34 publications

References 29 publications

Estimation of physicochemical characteristics and associated metal contamination risk in the Narmada River, India

Estimation of physicochemical characteristics and associated metal contamination risk in the Narmada River, India

An Endeavor of ‘Deep-underground Agriculture’: Deep-underground Storage in a Gold Mine Impacts the Germination of Canola (Brassica Napus L.) Seeds

Modelling of total dissolved solids in water supply systems using regression and supervised machine learning approaches

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