Water Quality Assessment Using Artificial Intelligence Techniques: SOM and ANN—A Case Study of Melen River Turkey

Şengörür, Bülent; Köklü, Rabia; Ateş, Asude

doi:10.1007/s12403-015-0163-9

Cited by 27 publications

(11 citation statements)

References 23 publications

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“…Cr was detected with a median concentration of

(n.d.–

), and a similar level was reported in Melen, Belgium (

; Sengorur et al. 2015 ).…”

Section: Discussionsupporting

confidence: 86%

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Occurrence and Health Risks of Organic Micro-Pollutants and Metals in Groundwater of Chinese Rural Areas

Tian

Shang

et al. 2020

Environ Health Perspect

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Background: Groundwater is a main drinking-water source for Chinese rural residents. The overall pollution status of organic micropollutants (OMPs) and metals in the groundwater and corresponding health risks are unknown. Objectives: Our objective was to comprehensively screen for and assess the health risks of OMPs and metals in groundwater of rural areas in China where groundwater is used for drinking so as to provide a benchmark for monitoring and improving groundwater quality in future developments. Methods: One hundred sixty-six groundwater samples were collected in the rural areas of China, and 1,300 OMPs and 25 metals were screened by GC-MS, LC-QTOF/MS, and ICP-MS analysis. To assess the noncarcinogenic and carcinogenic risks of the detected pollutants, missing toxicity threshold values were extrapolated from existing databases or predicted by quantitative structure–activity relationship (QSAR) models. Monte Carlo simulation was performed to account for uncertainties in the exposure parameters and toxicity thresholds. Results: Two hundred thirty-three OMPs and 25 metals were detected from the 166 samples. The concentration summation for the detected OMPs ranged from 2.9 to among the different sampling sites. Cumulative noncarcinogenic risks for the OMPs were estimated to be negligible. However, high metal risks were calculated in 23% of the sites. Forty-two carcinogens (including 38 OMPs) were identified and the cumulative carcinogenic risks in 34% of the sites were calculated to be (i.e., one excess cancer case in a population of 10 thousand people). The carcinogenic risks were estimated to be mainly associated with exposures to the metals, which were calculated to contribute 79% (0–100%) of the cumulative carcinogenic risks. Discussion: The overall status of OMPs and metals pollution in the groundwater and the corresponding health risks were determined preliminarily, which may provide a benchmark for future efforts in China to ensure the safety of drinking water for the local residents in rural areas. The joint application of QSARs and Monte Carlo simulation provided a feasible way to comprehensively assess the health risks of the large and ever-increasing number of pollutants detected in the aquatic environment. https://doi.org/10.1289/EHP6483

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“…Cr was detected with a median concentration of

(n.d.–

), and a similar level was reported in Melen, Belgium (

; Sengorur et al. 2015 ).…”

Section: Discussionsupporting

confidence: 86%

“…2017 ). The As concentrations in the present study (n.d.–

) were similar to those in the Melen watershed, Turkey (n.d.–

; Sengorur et al. 2015 ); lower than the concentrations reported in Yin Chuan, China (

; Han et al.…”

Section: Discussionmentioning

confidence: 70%

Occurrence and Health Risks of Organic Micro-Pollutants and Metals in Groundwater of Chinese Rural Areas

Tian

Shang

et al. 2020

Environ Health Perspect

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“…The number of neurons in the input and output layer depends on the number of input and output variables respectively (Sengorur et al, 2015). The number of input neurons varied from 1 to 4 representing the input parameters that affect the DO while the output layer has one neuron representing DO.…”

Section: Ann Architecture and Trainingmentioning

confidence: 99%

Dissolved oxygen modelling using artificial neural network: a case of River Nzoia, Lake Victoria basin, Kenya

Kanda

Kipkorir

Kosgei

2016

Journal of Water Security

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River Nzoia in Kenya, due to its role in transporting industrial and municipal wastes in addition to agricultural runoff to Lake Victoria, is vulnerable to pollution. Dissolved oxygen is one of the most important indicators of water pollution. Artificial neural network (ANN) has gained popularity in water quality forecasting. This study aimed at assessing the ability of ANN to predict dissolved oxygen using four input variables of temperature, turbidity, pH and electrical conductivity. Multilayer perceptron network architecture was used in this study. The data consisted of 113 monthly values for the input variables and output variable from 2009-2013 which were split into training and testing datasets. The results obtained during training and testing were satisfactory with R 2 varying from 0.79 to 0.94 and RMSE values ranging from 0.34 to 0.64 mg/l which imply that ANN can be used as a monitoring tool in the prediction of dissolved oxygen for River Nzoia considering the non-correlational relationship of the input and output variables. The dissolved oxygen values follow seasonal trend with low values during dry periods.

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“…For this reason, lately, non-linear multivariate methods have received significant attention from environmental researchers. Among several techniques, the self-organizing map (SOM) is one of the most adopted in the water quality field [20,22,23]. It is a type of artificial neural network (ANN) composed of fully connected neuron arrays, able to describe an environmental phenomenon depending on different physical variables (represented by a high-dimensional space) through a new low-dimensional space (usually two dimensions) [24].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

et al. 2021

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Urban stormwater runoff represents a significant challenge for the practical assessment of diffuse pollution sources on receiving water bodies. Given the high dimensionality of the problem, the main goal of this study was the comparison of linear and non-linear machine learning (ML) methods to characterize urban nutrient runoff from impervious surfaces. In particular, the principal component analysis (PCA) for the linear technique and the self-organizing map (SOM) for the non-linear technique were chosen and compared considering the high number of successful applications in the water quality field. To strengthen this comparison, these techniques were supported by well-known linear and non-linear methods. Those techniques were applied to a complete dataset with precipitation, flow rate, and water quality (sediments and nutrients) records of 577 events gathered for a watershed located in Southern Italy. According to the results, both linear and non-linear techniques can represent build-up and wash-off, the two main processes that characterize urban nutrient runoff. In particular, non-linear methods are able to capture and represent better the rainfall-runoff process and the transport of dissolved nutrients in urban runoff (dilution process). However, their computational time is higher than the linear technique (0.0054 s vs. 15.24 s, for linear and non-linear, respectively, in our study). The outcomes of this study provide significant insights into the application of ML methods for the water quality field.

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Water Quality Assessment Using Artificial Intelligence Techniques: SOM and ANN—A Case Study of Melen River Turkey

Cited by 27 publications

References 23 publications

Occurrence and Health Risks of Organic Micro-Pollutants and Metals in Groundwater of Chinese Rural Areas

Occurrence and Health Risks of Organic Micro-Pollutants and Metals in Groundwater of Chinese Rural Areas

Dissolved oxygen modelling using artificial neural network: a case of River Nzoia, Lake Victoria basin, Kenya

A Comparison of Linear and Non-Linear Machine Learning Techniques (PCA and SOM) for Characterizing Urban Nutrient Runoff

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