Prediction of groundwater quality using efficient machine learning technique

Singha, Sudhakar; Pasupuleti, Srinivas; Singha, Soumya S.; Singh, Rambabu; Kumar, Suresh

doi:10.1016/j.chemosphere.2021.130265

Cited by 208 publications

(53 citation statements)

References 66 publications

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“…The physicochemical parameters chosen in the current study may also pose a drawback due to possible inadequate sampling. In addition to this, the uncertainty problem of the physical-based models in water quality modeling is inevitable and has been discussed in many studies (Bui et al 2020;Kisi et al 2018;Singha et al 2021). Future research may add the use of different input physicochemical parameters to predict the WQI based on WHO guidelines, to compare with other standard indexes.…”

Section: Standardized Coefficient Variabelsmentioning

confidence: 99%

“…The AI technique is a potential and robust multifunctioning tool in water-science-related fields (Babbar and Babbar 2017;Kisi et al 2018;Kim et al, 2019;Bui et al 2020;Abba et al 2020;Hayder et al 2021;Singha et al 2021;Bilali et al 2021). Several research scholars have employed AI techniques worldwide including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) in different water-related studies.…”

Section: Introductionmentioning

confidence: 99%

“…Several research scholars have employed AI techniques worldwide including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) in different water-related studies. The RF model was applied for the groundwater quality prediction (Singha et al 2021), flood susceptibility study (Towfiqul Islam et al 2021), river water quality prediction (Asadollah et al 2021), and so on. Likewise, the SVM model was adopted for predicting marine water quality (Deng et al 2021) and wastewater treatment plant monitoring (Nourani et al 2018), with different precision levels.…”

Section: Introductionmentioning

confidence: 99%

“…Bui et al (2020) found the better predictive performance of hybrid AI models over the conventional models for predicting WQI with 4 conventional and 12 hybrid AI techniques. Recently, Singha et al (2021) applied deep learning for predicting WQI with 3 traditional models and found that the deep learning model is a more robust and accurate tool than the traditional model in the prediction of groundwater quality. Valentini et al (2021) introduced a new WQI equation for Mirim Lagoon and evaluated its suitability based on 154 samples collected over three years at seven sampling points in Mirim Lagoon.…”

Section: Introductionmentioning

confidence: 99%

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Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

et al. 2021

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Groundwater quality appraisal is one of the most crucial tasks to ensure safe drinking water sources. Concurrently, a water quality index (WQI) requires some water quality parameters. Conventionally, WQI computation consumes time and is often found with various errors during subindex calculation. To this end, 8 artificial intelligence algorithms, e.g., multilinear regression (MLR), random forest (RF), M5P tree (M5P), random subspace (RSS), additive regression (AR), artificial neural network (ANN), support vector regression (SVR), and locally weighted linear regression (LWLR), were employed to generate WQI prediction in Illizi region, southeast Algeria. Using the best subset regression, 12 different input combinations were developed and the strategy of work was based on two scenarios. The first scenario aims to reduce the time consumption in WQI computation, where all parameters were used as inputs. The second scenario intends to show the water quality variation in the critical cases when the necessary analyses are unavailable, whereas all inputs were reduced based on sensitivity analysis. The models were appraised using several statistical metrics including correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE). The results reveal that TDS and TH are the key drivers influencing WQI in the study area. The comparison of performance evaluation metric shows that the MLR model has the higher accuracy compared to other models in the first scenario in terms of 1, 1.4572*10–08, 2.1418*10–08, 1.2573*10–10%, and 3.1708*10–08% for R, MAE, RMSE, RAE, and RRSE, respectively. The second scenario was executed with less error rate by using the RF model with 0.9984, 1.9942, 3.2488, 4.693, and 5.9642 for R, MAE, RMSE, RAE, and RRSE, respectively. The outcomes of this paper would be of interest to water planners in terms of WQI for improving sustainable management plans of groundwater resources.

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Section: Standardized Coefficient Variabelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

et al. 2021

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“…In the last few decades, artificial neural networks (ANNs) have been widely applied in the area of water quality modeling. They are considered a prediction tool and have been widely used in various fields such as flood prediction [17,18], land use [19], and water quality [20], or to predict parameter values such as electrical conductivity and total dissolved solids based on other variables measurements [21][22][23][24][25][26]. They have also been used in hydrogeology to determine aquifer parameters [27][28][29], evaluate the qualitative characteristics of groundwater [30], and predict groundwater level [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

et al. 2021

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Due to the fact of water resource deterioration from human activities and increased demand over the last few decades, optimization of management practices and policies is required, for which more reliable data are necessary. Cost and time are always of importance; therefore, methods that can provide low-cost data in a short period of time have been developed. In this study, the ability of an artificial neural network (ANN) and a multiple linear regression (MLR) model to predict the electrical conductivity of groundwater samples in the GallikosRiver basin, northern Greece, was examined. A total of 233 samples were collected over the years 2004–2005 from 89 sampling points. Descriptive statistics, Pearson correlation matrix, and factor analysis were applied to select the inputs of the water quality parameters. Input data to the ANN and MLR were Ca, Mg, Na, and Cl. The best results regarding the ANN were provided by a model that included one hidden layer of three neurons. The mean absolute percentage error, modeling efficiency, and root mean square error were used to evaluate the performances of the methods and to compare the prediction capabilities of the ANN and MLR. We concluded that the ANN and MLR models were valid and had similar accuracy (using the same inputs) with a large number of samples, but in the case of a smaller data set, the MLR showed a better performance.

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Assessment of Human Health Risk in Baitarani Basin, Odisha Using Water Quality Index (WQI), Cluster Analysis (CA), and Geographic Information Systems (GIS)

Das

2024

Sustainable Development Using Geospatial Techniques

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Prediction of groundwater quality using efficient machine learning technique

Cited by 208 publications

References 66 publications

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

Assessment of Human Health Risk in Baitarani Basin, Odisha Using Water Quality Index (WQI), Cluster Analysis (CA), and Geographic Information Systems (GIS)

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