Particle swarm and grey wolf optimization: enhancing groundwater quality models through artificial neural networks

Sahour, Soheil; Khanbeyki, Matin; Gholami, Vahid; Sahour, Hossein; Karimi, Hadi; Mohammadi, Mohsen

doi:10.1007/s00477-023-02610-1

Cited by 8 publications

(1 citation statement)

References 57 publications

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“…Additionally, six ML classifiers, which includes SVM, XGB, RF, ANN, GCM (Gaussian Classifier Model) and KNN, which have been utilized to make relationships among GWQI and its controlling factors. Moreover, the study [26] has been proposed three ML methods including XGB, DNN and MLR (Multiple Linear Regression) have been utilized to determine the nitrate pollution in groundwater in North Iran. The results have been shown that the evaporation rates, groundwater depth and population density are the significant factors influencing groundwater nitrate pollution.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration Analysis

2024

JMC

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Human actions and changing weather patterns are contributing to the growing demand for groundwater resources. Nevertheless, evaluating the quality of groundwater is crucial. Nitrate is a significant water contaminant that can lead to blue-baby syndrome or methemoglobinemia. Therefore, it is necessary to assess the level of nitrate in groundwater. Current methods involve evaluating the quality of groundwater and integrating it into the models. The inappropriate datasets, lack of performance, and other constraints are limitations of current methods. Ground water dataset is used and pre-processed the data’s. Selected data’s are feature extracted and associated with the rule ranking. In the suggested model, the use of associative rule mining technique has been implemented to address these challenges and assess nitrate levels in groundwater. The method of rule ranking is carried out using association rule mining technique to divide the datasets. The split gini indexing algorithm is introduced in the proposed model for data classification. The Split Gini Indexing algorithm is a decision tree induction algorithm that is used to build decision trees for classification tasks. It is based on the Gini impurity measure, which measures the heterogeneity of a dataset. The quality of groundwater has been classified using Naïve Bayes, SVM, and KNN algorithms. The proposed approach's efficiency is evaluated by calculating performance metrics such as precision, accuracy, F1-score, and recall values. The suggested method in the current research attains an improved accuracy of 0.99, demonstrating enhanced performance.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration Analysis

2024

JMC

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Hybrid modeling techniques for predicting chemical oxygen demand in wastewater treatment: a stacking ensemble learning approach with neural networks

Tuppad

2024

Environ Monit Assess

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Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods

Agbasi,

Abu,

Egbueri

2024

Environ Dev Sustain

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Particle swarm and grey wolf optimization: enhancing groundwater quality models through artificial neural networks

Cited by 8 publications

References 57 publications

Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration Analysis

Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration Analysis

Hybrid modeling techniques for predicting chemical oxygen demand in wastewater treatment: a stacking ensemble learning approach with neural networks

Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods

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