Neural network modelling for nitrate concentration in groundwater of Kadava River basin, Nashik, Maharashtra, India

Wagh, Vasant; Panaskar, D. B.; Muley, Aniket; Mukate, Shrikant; Gaikwad, Satyajit

doi:10.1016/j.gsd.2017.12.012

Cited by 95 publications

(32 citation statements)

References 23 publications

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“…Moreover, the result of the component loadings shows that all the parameters have very high positive loadings on the extracted component (Table 9). Strong positive loadings of extracted factors in only PC imply lithogenic and anthropogenic sources of heavy metals in analyzed groundwater samples (Barzegar et al 2017;Wagh et al 2018). Based on the component loadings' scatter plot (Fig.…”

Section: Statistical Analysis Of Groundwater Datamentioning

confidence: 99%

Assessment of heavy metals contamination and associated risks in shallow groundwater sources from three different residential areas within Ibadan metropolis, southwest Nigeria

2021

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This study has been conducted to appraise the concentrations of selected heavy metals and total dissolved solids (TDSs) in the drinking water from shallow wells in parts of Ibadan metropolis, southwest Nigeria. Fifteen (15) water samples were collected from three representative residential locations [traditional core area (TCA), peri-urban area (PUA), and urban area (UA)] for geochemical analysis. Heavy metals and TDS were analyzed with the aid of atomic absorption spectrophotometer and calibrated meter, respectively. The mean concentration (mg/L) of Zn, Pb Mn, Fe, and Cd has been 3.930, 0.658, 0.0304, 1.698, and 0.501, respectively, and as a consequence, the order of abundance of studied metals was Zn > Fe > Pb > Cd > Mn. Concentrations of Zn, Fe, Pb, and Cd were higher than recommended standards in 60%, 86.7%, 100%, and 100% of groundwater samples, respectively. However, at all points tested, the mean concentrations of Mn and TDS in water samples lie within the safe limits set by World Health Organization. The evaluation of geoaccumulation index (Igeo), enrichment factor (EF), and contamination factor suggests that representative water samples were low-to-moderate contamination. The potential ecological risk index advocates low-to-moderate ecological risk in TCA and PUA, while it demonstrated exclusive “moderate” risk in UA. Further, the range of pollution load index (PLI) (0.55–1.32) in both TCA and PUA shows nil-to-moderate pollution status, while PLI values > 1 in UA indicate moderate contaminated state. The degree of contamination in groundwater showed the following trends: UA > TCA > PUA in the study area. Moreover, the results of EF and quantification of contamination of analyzed metals in water samples indicate geogenic and anthropogenic inputs. The contribution of studied metals to the incidence of non-cancer risk via oral intake within the residential sites follows the order: cadmium > lead > zinc > iron > manganese. The hazard index as a result of ingested heavy metals for the three population classes surpasses the acceptable range in the order of infant < child < adult. Cadmium and lead made considerable impact to the estimation of cancer risk in the study area for the three human population categories. Factor analysis extracted only one component that explained 94.64% of the entire variance, while cluster analysis identified three distinct groups based on similar water quality characteristics. Based on the findings of the study, awareness programs toward protecting the shallow groundwater sources should be launched, encouraged, and sustained. Moreover, the study suggests better hygienic practices and pre-treatment of contaminated water before consumption.

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Section: Statistical Analysis Of Groundwater Datamentioning

confidence: 99%

Assessment of heavy metals contamination and associated risks in shallow groundwater sources from three different residential areas within Ibadan metropolis, southwest Nigeria

2021

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“…Along with the development of ML technology, non-linear modelling is widely used in researches, including the geological, environmental, and engineering area. In order to deal with problems of surface water and ground water, the SVM method is applied to predict water quality and water level [23,24], ANN and DT are used to predict the [NO 3 − ] of ground water [25], set up the water quality monitoring system [26]. Boosting tree is also used to classify distributed water and ground water.…”

Section: Supervised Machine Learning Algorithmmentioning

confidence: 99%

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

Shan¹

2021

Groundwater Management and Resources

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Water inrush is a major threat to the working safety for coal mines in the Northern China coal district. The inrush pattern, threaten level, and also the geochemical characteristics varies according to the different of water sources. Therefore, identifying the water source correctly is an important task to predict and control the water inrush accidents. In this chapter, the algorithms and attempts to identify the water inrush sources, especially in the Northern China coal mine district, are reviewed. The geochemical and machine learning algorithms are two main methods to identify the water inrush sources. Four main steps need to apply, namely data processing, feature selection, model training, and evaluation, in the process of machine learning (ML) modelling. According to a calculation instance, most of the major ions, and some trace elements, such as Ti, Sr, and Zn, were identified to be important in light of geochemical analysis and machine learning modelling. The ML algorithms, such as random forest (RF), support vector machine (SVM), Logistica regression (LR) perform well in the source identification of coal mine water inrush.

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“…Some studies relate to the prediction of chemical desulfurisation of Tabas coal and the prediction of leaching recovery for Al 2 O 3 with ANNs. 30,31 An ANN has been used to estimate nitrate concentration in groundwater 32 and the concentration of major ions in rivers 33 . Hoseinian et al 34 developed the ANN model for predicting column leaching recovery of copper by considering four leaching parameters as inputs to the model, namely, column height, particle size, acid flow rate, and leaching time.…”

Section: Introductionmentioning

confidence: 99%

Application of Taguchi method and artificial neural network model for the prediction of reductive leaching of cobalt(III) from oxidised low-grade ores

Kasongo

Mwanat

2021

S. Afr. J. Sci.

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The leaching process of cobalt using a wide range of experimental variables is described. The treated cobalt samples were from the Kalumbwe Mine in the south of the Democratic Republic of Congo. In this study, a predictive model of cobalt recovery using both the Taguchi statistical method and an artificial neural network (ANN) algorithm was proposed. The Taguchi method utilising a L25 (55) orthogonal array and an ANN multi-layer, feed-forward, back-propagation learning algorithm were adopted to optimise the process parameters (acid concentration, leaching time, temperature, percentage solid, and sodium metabisulfite concentration) responsible for the high recovery of cobalt by reducing sulfuric acid leaching. The ANN was built with a neuron in the output layer corresponding to the cobalt leaching recovery, 10 hidden layers, and 5 input variables. The validation of the ANN model was performed with the results of the Taguchi method. The optimised trained neural network depicts the testing data and validation data with R2 equal to 1 and 0.5676, respectively.

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Neural network modelling for nitrate concentration in groundwater of Kadava River basin, Nashik, Maharashtra, India

Cited by 95 publications

References 23 publications

Assessment of heavy metals contamination and associated risks in shallow groundwater sources from three different residential areas within Ibadan metropolis, southwest Nigeria

Assessment of heavy metals contamination and associated risks in shallow groundwater sources from three different residential areas within Ibadan metropolis, southwest Nigeria

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

Application of Taguchi method and artificial neural network model for the prediction of reductive leaching of cobalt(III) from oxidised low-grade ores

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