Prediction and evaluation of groundwater characteristics using the radial basic model in Semi-arid region, India

Panneerselvam, Balamurugan; Muniraj, Kirubakaran; Pande, Chaitanya B.; Ravichandran, Nagavinothini

doi:10.1080/03067319.2021.1873316

Cited by 46 publications

(5 citation statements)

References 34 publications

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“…The Gibbs plot for the study area shows that 90% of the sample locations was highly influenced by rock-water interactions, and the remaining 10% of the sample locations was dominated by evaporation. The increasing impacts of parent rock weathering, oxidation-reduction, ion exchange, and mineral dissolution, such as calcite and dolomite dissolution, constituted the major factors resulting in groundwater quality deterioration (Panneerselvam et al, 2021a, b). Twentyeight samples revealed calcite dissolution, and two samples fall indicated dolomite dissolution (Fig.…”

Section: Gibbs Plotmentioning

confidence: 99%

“…Approximately 80% of health issues and diseases worldwide can be attributed to the consumption of contaminated water for domestic purposes (Karunanidhi et al, 2019). Groundwater quality assessment could facilitate the development of groundwater protection strategies against further pollution (Balamurugan et al, 2020d;Johnny et al, 2018;Muniraj et al, 2021;Panneerselvam et al, 2021a, b). Recently, both natural and artificial sources of groundwater contamination have caused human health issues, which is the most critical problem in arid and semiarid regions worldwide (Balamurugan et al, 2020a;Patil et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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An integrated approach to explore the suitability of nitrate-contaminated groundwater for drinking purposes in a semiarid region of India

Panneerselvam

Kirubakaran²,

Karunanidhi³

et al. 2022

Environ Geochem Health

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The main objective of the present study is to perform risk assessment of groundwater contaminated by nitrate (NO3−) and evaluate the suitability of groundwater for domestic purposes in the Palani region of South India. Thirty groundwater samples were collected in the study area. Various groundwater quality analysis parameters such as the pH, electrical conductivity, total dissolved solids, total hardness, major cations (Ca2+, Mg2+, Na+, and K+), and major anions (Cl−, SO42−, F−, CO32−, and HCO3−) were adopted in this study to evaluate the drinking water suitability according to 2011 World Health Organization (WHO) standards. Piper and Gibbs’s diagrams for the tested groundwater indicated that, due to the influence of rock–water interactions, evaporation, and reverse ion exchange, the chemical composition of groundwater varied. According to water quality index (WQI) mapping results, 46.67% of the sample locations was identified as contaminated zones via GIS spatial analysis. Multivariate statistical analysis methods, such as principal component analysis, cluster analysis, and the Pearson correlation matrix, were applied to better understand the relationship between water quality parameters. The results demonstrated that 40% of the samples could be identified as highly affected zones in the study region due to a high nitrate concentration. The noncarcinogenic health risks among men, women, and children reached 40, 50, and 53%, respectively. The results illustrated that children and women occurred at a higher risk than did men in the study region. The major sources of contamination included discharge from households, uncovered septic tanks, leachate from waste dump sites, and excess utilization of fertilizers in the agricultural sector. Furthermore, using the nitrate health hazard integrated method with the conventional indexing approach ensures that groundwater reliability can be guaranteed, contamination can be explored, and appropriate remedial measures can be implemented.

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Section: Gibbs Plotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An integrated approach to explore the suitability of nitrate-contaminated groundwater for drinking purposes in a semiarid region of India

Panneerselvam

Kirubakaran²,

Karunanidhi³

et al. 2022

Environ Geochem Health

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“…Several works have considered underground water for water quality analysis and presented machine learning based techniques such as Panneerselvam et al [17] identified that the rock-water interaction and ion exchange are also considered as important factors which affect the water quality. To identify their impact authors developed a Hierarchical Cluster Analysis (HCA) and K-mean cluster analysis based approach.…”

Section: Literature Surveymentioning

confidence: 99%

Ensemble Tree Classifier based Analysis of Water Quality for Layer Poultry Farm: A Study on Cauvery River

Deepika¹,

Nagarathna²,

Channegowda³

2022

IJACSA

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Indian poultry industry has evolved from a simple backyard occupation to a large commercial agri-based enterprise. Chicken dominates poultry production in India, accounting for almost 95% of total egg production. Several factors affect the egg production such as feeding material, drinking water, environmental factors etc. Analyzing the water quality is one of the important tasks. Cauvery River is considered as the study area because of its importance in several states of South India which have significant contribution in poultry farming. The aim of the proposed study is to develop an automated approach of water quality analysis and present a novel machine learning approach which considers an improved feature ranking method and ensemble tree classifier with majority voting. The experimental result shows that proposed approach performs better with an accuracy of 95.12%.

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“…Some machine learning algorithms such as support vector machine (SVM), radial basis function neural network (RB-NN) and backpropagation neural network (BP-NN) have been integrated and compared to predict the water quality in constructed wetlands (Mohammadpour et al 2015). Panneerselvam et al (2021) in their study assessed the drinking water quality of Maharashtra, India using the RB-NN. An ideal model for irrigation water quality estimation have been developed by integrating ANN and multiple linear regression models (MLR) (Yildiz and Karakuş 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach

Omeka

2023

Environ Sci Pollut Res

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Poor irrigation water quality can mar agricultural productivity. Appraising the irrigation water quality requires the computation of various conventional quality parameters which are often time-consuming and associated with errors during sub-index computation. It becomes critical therefore, to have a visual assessment of the irrigation water quality and identify the most influential water quality parameters for accurate prediction, management, and sustainability of irrigation water quality. The overlay weighted sum technique was used to generate the irrigation water quality (IWQ) map of the area. The map revealed that 72.5% of the area (within the southeastern parts) were suitable for irrigation while 28.4% (found in isolated traces) were unsuitable. Multilayer perceptron artificial neural networks (MLP-ANNs) and multiple linear regression models (MLR) were integrated and validated to predict the IWQ parameters using Cl -, HCO3 -SO4 2-, NO3 -, Ca 2+ , Mg 2+ , Na + , K + , pH, EC, TH and TDS as input variables, and PI, MAR, SAR, PI, KR, SSP, and PS as output variables. The two models showed high performance accuracy based on the results of the coefficient of determination (R 2 = 0.513-0.983). Low modeling errors were observed from results of the sum of square errors (SOSE), relative errors (RE), adjusted R-square (R 2 adj), and residual plots; further confirming the efficacy of the two models, although the MLP-ANNs showed higher prediction accuracy with respect to R 2 . Based on the sensitivity of the MLP-ANN model, HCO3, pH, SO4, EC, and Cl were identified to have the greatest influence on the irrigation water quality of the area. This study has shown that the integration of GIS and Machine Learning can serve as rapid decision tools for proper planning and enhanced agricultural productivity.

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Prediction and evaluation of groundwater characteristics using the radial basic model in Semi-arid region, India

Cited by 46 publications

References 34 publications

An integrated approach to explore the suitability of nitrate-contaminated groundwater for drinking purposes in a semiarid region of India

An integrated approach to explore the suitability of nitrate-contaminated groundwater for drinking purposes in a semiarid region of India

Ensemble Tree Classifier based Analysis of Water Quality for Layer Poultry Farm: A Study on Cauvery River

Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach

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