Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration Analysis
Siddthan R
Siddthan R
Department of Computer Science, J.J.College of Arts & Science (Autonomous)(Affiliated to Bharathidasan University), Pudukkotai, Tamil Nadu, India.
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.
T. G. Nguyen, K. A. Phan, and T. H. N. Huynh, “Application of Integrated-Weight Water Quality Index in Groundwater Quality Evaluation,” Civil Engineering Journal, vol. 8, no. 11, pp. 2661–2674, Nov. 2022, doi: 10.28991/cej-2022-08-11-020.
H. Soleimani et al., “Groundwater quality evaluation and risk assessment of nitrate using monte carlo simulation and sensitivity analysis in rural areas of Divandarreh County, Kurdistan province, Iran,” International Journal of Environmental Analytical Chemistry, vol. 102, no. 10, pp. 2213–2231, Apr. 2020, doi: 10.1080/03067319.2020.1751147.
S. He, J. Wu, D. Wang, and X. He, “Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest,” Chemosphere, vol. 290, p. 133388, Mar. 2022, doi: 10.1016/j.chemosphere.2021.133388.
S. Shukla and A. Saxena, “Sources and Leaching of Nitrate Contamination in Groundwater,” Current Science, vol.118, no. 6, p. 883, Mar.2020, doi: 10.18520/cs/v118/i6/883-891.
Q. Zhang, H. Qian, P. Xu, W. Li, W. Feng, and R. Liu, “Effect of hydrogeological conditions on groundwater nitrate pollution and human health risk assessment of nitrate in Jiaokou IrrigationDistrict,” Journal of Cleaner Production, vol. 298, p. 126783, May 2021, doi: 10.1016/j.jclepro.2021.126783.
L. Knoll, L. Breuer, and M. J. E. R. L. Bach, "Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning," vol. 15, no. 6,p. 064004, 2020.
K. Fatah, “Evaluation Groundwater Quality By Using Gis And Water Quality Index Techniques For Wells In Bardarash Area, Northern Iraq,” Iraqi Geological Journal, vol. 53, no. 2C, pp. 87–104, Sep. 2020, doi: 10.46717/igj.53.2c.7rs-2020-09.07.
K. A. Ahmed, M. El-Rawy, A. M. Ibraheem, N. Al-Arifi, and M. K. Abd-Ellah, “Forecasting of Groundwater Quality by Using Deep Learning Time Series Techniques in an Arid Region,” Sustainability, vol. 15, no. 8, p. 6529, Apr. 2023,doi: 10.3390/su15086529.
S. S. Band et al., “Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration,” Sensors, vol. 20, no. 20, p. 5763, Oct. 2020, doi: 10.3390/s20205763.
L. Knoll, L. Breuer, and M. Bach, “Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning,” Environmental Research Letters, vol. 15, no. 6, p. 064004, May 2020, doi: 10.1088/1748-9326/ab7d5c.
S. Bedi, A. Samal, C. Ray, and D. Snow, “Comparative evaluation of machine learning models for groundwater quality assessment,” Environmental Monitoring and Assessment, vol. 192, no. 12, Nov. 2020, doi: 10.1007/s10661-020-08695-3.
Y. Teng, R. Zuo, Y. Xiong, J. Wu, Y. Zhai, and J. Su, “Riskassessment framework for nitrate contamination in groundwater for regional management,” Science of The Total Environment, vol. 697, p. 134102, Dec. 2019, doi: 10.1016/j.scitotenv.2019.134102.
S. D. S. Putro and W. Wilopo, “Assessment of nitrate contamination and its factors in the urban area of Yogyakarta, Indonesia,” Journal of Degraded and Mining Lands Management, vol. 9, no. 4, p. 3643, Jul. 2022, doi: 10.15243/jdmlm.2022.094.3643.
H. E. Elzain et al., “Novel machine learning algorithms to predict the groundwater vulnerability index to nitrate pollution at two levels of modeling,” Chemosphere, vol. 314, p. 137671, Feb. 2023, doi: 10.1016/j.chemosphere.2022.137671.
R. Haggerty, J. Sun, H. Yu, and Y. J. W. R. Li, "Application of machine learning in groundwater quality modeling-A comprehensive review," vol. 233, p. 119745, 2023.
Mazraeh, M. Bagherifar, S. Shabanlou, and R. Ekhlasmand, “A Hybrid Machine Learning Model for Modeling Nitrate Concentration in Water Sources,” Water, Air, & Soil Pollution, vol.234, no. 11, Nov. 2023, doi: 10.1007/s11270-023-06745-3.
M. Ghaderzadeh, M. Aria, and F. Asadi, “X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic,” BioMed Research International, vol. 2021, pp. 1–16, Aug. 2021, doi: 10.1155/2021/9942873.
Bhattarai, S. Dhakal, Y. Gautam, and R. Bhattarai, “Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse,” Water, vol. 13, no. 21, p. 3096, Nov. 2021, doi: 10.3390/w13213096.
R. Haggerty, J. Sun, H. Yu, and Y. Li, “Application of machine learning in groundwater quality modeling -A comprehensive review,” Water Research, vol. 233, p. 119745, Apr. 2023, doi: 10.1016/j.watres.2023.119745.
S. Sahour etal., "Evaluation of machine learning algorithms for groundwater quality modeling," vol. 30, no. 16, pp. 46004-46021, 2023.
R. Md. T. Islam et al., “Application of novel framework approach for prediction of nitrate concentration susceptibility in coastal multi-aquifers, Bangladesh,” Science of The Total Environment, vol. 801, p. 149811, Dec. 2021, doi: 10.1016/j.scitotenv.2021.149811.
S. C. Pal, D. Ruidas, A. Saha, A. R. Md. T. Islam, and I. Chowdhuri, “Application of novel data-mining technique based nitrate concentration susceptibility prediction approach for coastal aquifers in India,” Journal of Cleaner Production, vol. 346, p. 131205, Apr. 2022, doi: 10.1016/j.jclepro.2022.131205.
H. Raheja, A. Goel, and M. Pal, “Prediction of groundwater quality indices using machine learning algorithms,” Water Practiceand Technology, vol. 17, no. 1, pp. 336–351, Dec. 2021, doi: 10.2166/wpt.2021.120.
P. Agrawal et al., “Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment,” Water, vol. 13, no. 9, p. 1172, Apr. 2021, doi: 10.3390/w13091172.
S. Sahour, M. Khanbeyki, V. Gholami, H. Sahour, I. Kahvazade, and H. Karimi, “Evaluation of machine learning algorithms for groundwater quality modeling,” Environmental Science and Pollution Research, vol. 30, no. 16, pp. 46004–46021, Jan. 2023, doi: 10.1007/s11356-023-25596-3.
S. Sahour, M. Khanbeyki, V. Gholami, H. Sahour, H. Karimi, and M. Mohammadi, “Particle swarm and grey wolf optimization: enhancing groundwater quality models through artificial neural networks,” Stochastic Environmental Research and Risk Assessment, vol. 38, no. 3, pp. 993–1007, Nov. 2023, doi: 10.1007/s00477-023-02610-1.
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Author(s) thanks to Dr.Shanthi PM for this research completion and support.
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Siddthan R
Siddthan R
Department of Computer Science, J.J.College of Arts & Science (Autonomous)(Affiliated to Bharathidasan University), Pudukkotai, Tamil Nadu, India.
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Cite this article
Siddthan R and Shanthi PM, “Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration Analysis”, Journal of Machine and Computing, pp. 702-721, July 2024. doi: 10.53759/7669/jmc202404067.
