Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms

Egbueri, Johnbosco C.; Agbasi, Johnson C.

doi:10.1007/s11356-022-18520-8

Cited by 74 publications

(20 citation statements)

References 108 publications

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“…Driven by big data, scholars in this field have also rapidly joined the ranks of big data research. Big data research has reshaped people's understanding of data, and also promoted the transformation of academic research from traditional theoretical empirical and computational research models to data-driven research paradigms [ 8 ]. Therefore, the arrival of the big data era seems to have had a considerable effect on the evolution of the library and information domain.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Hotspot Mining in the Field of Library and Information Science under the Environment of Big Data

Jiang

2022

Journal of Environmental and Public Health

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Currently, with the implementation of big data strategies in countries all over the world, big data has achieved vigorous development in various fields. Big data research and application practices have also rapidly attracted the attention of the library and information field. Objective. The study explored the current state of research and research hotspots of big data in the library and information field and further discussed the future research trends. Methods. In the CNKI database, 16 CSSCI source journals in the discipline of library information and digital library were selected as data sources, and the relevant literature was retrieved with the theme of “big data.” The collected literature was excluded and expanded according to the citation relationship. Then, with the help of Bicomb and SPSS, co-word analysis and cluster analysis would be carried out on these literature results. Results. According to the findings of the data analysis, the research hotspots on the topic mainly focus on five major research themes, namely, big data and smart library, big data and intelligence research, data mining and cloud computing, big data and information analysis, and library innovation and services. Limitations. At present, the research scope and coverage on this topic are wide, which leads to the research still staying at the macro level. Conclusions. Big data research will remain one of the hotspots in the future. However, the most study is still limited to the perspective of library and information and has not yet analyzed the research status, research hotspots, and development trends in this field from the perspective of big data knowledge structure. Moreover, machine learning, artificial intelligence, knowledge services, AR, and VR may be new directions for future attention and development.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Hotspot Mining in the Field of Library and Information Science under the Environment of Big Data

Jiang

2022

Journal of Environmental and Public Health

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“…The maximum r-value of 0.85 was achieved in their study. Egbueri and Agbasi [31] used a comparison study of MLR and ANN on parameters such as pH, TDS, EC, total hardness, modified heavy metal index (MHMI), pollution load index (PLI), and synthetic pollution index (SPI) in their study to evaluate the groundwater quality. Their results indicated that for estimation of EC, TDS, and total hardness, MLR performed better than the ANN prediction model.…”

Section: Prediction By Ann Model For Three Sub-divisionsmentioning

confidence: 99%

Assessment of Algorithm Performance on Predicting Total Dissolved Solids Using Artificial Neural Network and Multiple Linear Regression for the Groundwater Data

Farooq

Zafar

Raheem

et al. 2022

Water

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Estimating groundwater quality parameters through conventional methods is time-consuming through laboratory measurements for megacities. There is a need to develop models that can help decision-makers make policies for sustainable groundwater reserves. The current study compared the efficiency of multivariate linear regressions (MLR) and artificial neural network (ANN) models in the prediction of groundwater parameters for total dissolved solids (TDS) for three sub-divisions in Lahore, Pakistan. The data for this study were collected every quarter of a year for six years. ANN was applied to investigate the feasibility of feedforward, backpropagation neural networks with three training functions T-BR (Bayesian regularization backpropagation), T-LM (Levenberg–Marquardt backpropagation), and T-SCG (scaled conjugate backpropagation). Two activation functions were used to analyze the performance of algorithmic training functions, i.e., Logsig and Tanh. Input parameters of pH, electrical conductivity (EC), calcium (Ca2+), magnesium (Mg2+), chloride (Cl−), and sulfate (SO42−) was used to predict TDS as an output parameter. The computed values of TDS by ANN and MLR were in close agreement with their respective measured values. Comparative analysis of ANN and MLR showed that TDS root means square error (RMSE) for city sub-division and Pearson’s coefficient of correlation (r) for ANN and MLR were 2.9% and 0.981 and 4.5% and 0.978, respectively. Similarly, for the Farrukhabad sub-division, RMSE and r for ANN were 4.9% and 0.952, while RMSE and r for MLR were 5.5% and 0.941, respectively. For the Shahadra sub-division, RMSE was 10.8%, r was 0.869 for ANN, RMSE was 11.3%, and r was 0.860 for MLR. The results exhibited that the ANN model showed less error in results than MLR. Therefore, ANN can be employed successfully as a groundwater quality prediction tool for TDS assessment.

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“…Each water quality index has a different classification scheme. Nevertheless, most have a strong agreement (Egbueri & Agbasi, 2022b ). Since the quality of groundwater resources is rated by the concentration levels of various water quality parameters, prediction of future occurrences of these parameters will enhance the forecasting of groundwater quality.…”

Section: Introductionmentioning

confidence: 99%

“…To mitigate the degradation of water resources, potential sources, pathways, and future possibilities of contamination need to be identified. Numerous data-driven (numerical, graphical, statistical, and machine learning) approaches have been applied to identify the possible sources of contamination (Wagh et al, 2016 , 2017b ; Ansari & Umar, 2019 ; Egbueri, 2019 , 2020 ; Enyigwe et al 2021 ), pathways of contaminants (Egbueri & Agbasi, 2022a , 2022b ; Wang et al, 2012 ; Yang et al, 2020 ), and to forecast the chances of reoccurrence of these contaminants in water resources (Wagh et al, 2017b ; Alizamir & Sobhanardakani, 2017a , 2017b ; Egbueri & Agbasi, 2022b ).…”

Section: Introductionmentioning

confidence: 99%

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

Agbasi

Egbueri

2023

J. Sediment. Environ.

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Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria, works that have used data-driven algorithms in predicting PTEs in groundwater are scarce. In addition, only a few works have simulated water quality indices using machine learning modelling methods in the region. Therefore, in this study, physicochemical analyses were carried out on groundwater samples in southeastern Nigeria. The laboratory results were used to compute two water quality indices: pollution index of groundwater (PIG) and the water pollution index (WPI), to ascertain groundwater quality. In addition, the physicochemical parameters served as input variables for multiple linear regression (MLR) and artificial neural network (ANN) modelling and prediction of Cr, Fe, Ni, NO 3 − , Pb, Zn, WPI, and PIG. The results of WPI and PIG computation showed that about 30–35% of the groundwater samples were unsuitable for human consumption, whereas 65–70% of the samples were deemed suitable. The insights from the PIG and WPI model also revealed that lead (Pb) was the most influential PTE that degraded the quality of groundwater resources in the research area. The findings of the MLR and ANN models indicated strong positive prediction accuracies ( R 2 = 0.856–1.000) with low modeling errors. The predictive MLR and ANN models of the PIG and WPI generally outperformed those of the PTEs. The models produced in this study predicted the PTEs better compared to previous studies. Thus, this work provides insights into effective water sustainability, management, and protection.

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Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms

Cited by 74 publications

References 108 publications

[Retracted] Hotspot Mining in the Field of Library and Information Science under the Environment of Big Data

[Retracted] Hotspot Mining in the Field of Library and Information Science under the Environment of Big Data

Assessment of Algorithm Performance on Predicting Total Dissolved Solids Using Artificial Neural Network and Multiple Linear Regression for the Groundwater Data

Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study

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