A Novel Method to Predict the Nitrate Concentration Level in Groundwater Using the Associative Rule Mining Algorithm with Random Forest Classification Approach
A Novel Method to Predict the Nitrate Concentration Level in Groundwater Using the Associative Rule Mining Algorithm with Random Forest Classification Approach |
||
 |
 |
|
| © 2024 by IJETT Journal | ||
| Volume-72 Issue-1 |
||
| Year of Publication : 2024 | ||
| Author : R. Siddthan, PM. Shanthi |
||
| DOI : 10.14445/22315381/IJETT-V72I1P130 | ||
How to Cite?
R. Siddthan, PM. Shanthi, "A Novel Method to Predict the Nitrate Concentration Level in Groundwater Using the Associative Rule Mining Algorithm with Random Forest Classification Approach," International Journal of Engineering Trends and Technology, vol. 72, no. 1, pp. 311-324, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I1P130
Abstract
In the past few years, the prediction of concentration in groundwater has received top emphasis in research on water resource management and pollution control. Therefore, the objective of the current research is to employ data mining techniques like the Random Forest (RF) methodology to determine the susceptibility zones of coastal districts in eastern India. Utilizing multi-collinearity analysis, fifteen conditioning parameters have been determined, and the association rule mining approach was used to determine the relative importance in order to create a groundwater concentration susceptibility map. To prepare the inventory dataset and related modeling purposes, the four K-Fold Cross Validation (CV) technique’s resampling approach was applied. For assessing the effectiveness of all utilized models, seven statistical methodologies comprising receiver operating characteristics-area under curve (ROC-AUC) were employed. The study’s findings indicated that boosting is the methodology that performs best for defining groundwater concentration susceptibility maps (GNCSMs) at the regional level. The results guarantee that the RF model is more effective compared to the boosting and bagging approach.
Keywords
Nitrate, RF, Performance, Prediction, Groundwater, Associative Rule Mining, Random Forest Classification.
References
[1] Victor F. Rodriguez-Galiano et al., “Feature Selection Approaches for Predictive Modelling of Groundwater Nitrate Pollution: An Evaluation of Filters, Embedded and Wrapper Methods,” Science of the Total Environment, vol. 624, pp. 661-672, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Shine Bedi et al., “Comparative Evaluation of Machine Learning Models for Groundwater Quality Assessment,” Environmental Monitoring and Assessment, vol. 192, pp. 1-23, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Omid Rahmati et al., “Predicting Uncertainty of Machine Learning Models for Modelling Nitrate Pollution of Groundwater Using Quantile Regression and UNEEC Methods,” Science of the Total Environment, vol. 688, pp. 855-866, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Khabat Khosravi et al., “A Comparison Study of DRASTIC Methods with Various Objective Methods for Groundwater Vulnerability Assessment,” Science of the Total Environment, vol. 642, pp. 1032-1049, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Jawad S. Alagha, Md Azlin Md Said, and Yunes Mogheir, “Modeling of Nitrate Concentration in Groundwater Using Artificial Intelligence Approach—A Case Study of Gaza Coastal Aquifer,” Environmental Monitoring and Assessment, vol. 186, pp. 35-45, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Muhammad Awais et al., “Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach,” Applied Sciences, vol. 11, no. 21, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Maryam Torkashvand et al., “New Hybrid Evolutionary Algorithm for Optimizing Index-Based Groundwater Vulnerability Assessment Method,” Journal of Hydrology, vol. 598, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Maryam Torkashvand et al., “DRASTIC Framework Improvement Using Stepwise Weight Assessment Ratio Analysis (SWARA) and Combination of Genetic Algorithm and Entropy,” Environmental Science and Pollution Research, vol. 28, pp. 46704-46724, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Katherine M. Ransom et al., “A Hybrid Machine Learning Model to Predict and Visualize Nitrate Concentration throughout the Central Valley Aquifer, California, USA,” Science of the Total Environment, vol. 601-602, pp. 1160-1172, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[10] G. Charulatha et al., “Evaluation of Ground Water Quality Contaminants Using Linear Regression and Artificial Neural Network Models,” Arabian Journal of Geosciences, vol. 10, pp. 1-9, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Song He et al., “Predictive Modeling of Groundwater Nitrate Pollution and Evaluating its Main Impact Factors Using Random Forest,” Chemosphere, vol. 290, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Vasant Wagh et al., “Neural Network Modelling for Nitrate Concentration in Groundwater of Kadava River Basin, Nashik, Maharashtra, India,” Groundwater for Sustainable Development, vol. 7, pp. 436-445, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Khalifa M. Alkindi et al., “Prediction of Groundwater Nitrate Concentration in a Semiarid Region Using Hybrid Bayesian Artificial Intelligence Approaches,” Environmental Science and Pollution Research, vol. 29, pp. 20421-20436, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Raana Javidan, and Narges Javidan, “A Novel Artificial Intelligence-Based Approach for Mapping Groundwater Nitrate Pollution in the Andimeshk-Dezful Plain, Iran,” Geocarto International, vol. 37, no. 25, pp. 10434-10458, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Asma El Amri et al., “Nitrate Concentration Analysis and Prediction in a Shallow Aquifer in Central-Eastern Tunisia Using Artificial Neural Network and Time Series Modelling,” Environmental Science and Pollution Research, vol. 29, pp. 43300-43318, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Mehdi Jamei et al., “Developing Hybrid Data-Intelligent Method Using Boruta-Random Forest Optimizer for Simulation of Nitrate Distribution Pattern,” Agricultural Water Management, vol. 270, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Fabio Di Nunno et al., “A Nonlinear Autoregressive Exogenous (NARX) Model to Predict Nitrate Concentration in Rivers,” Environmental Science and Pollution Research, vol. 29, pp. 40623-40642, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Hussam Eldin Elzain et al., “ANFIS-MOA Models for the Assessment of Groundwater Contamination Vulnerability in a Nitrate Contaminated Area,” Journal of Environmental Management, vol. 286, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Mosleh Hmoud Al-Adhaileh, and Fawaz Waselallah Alsaade, “Modelling and Prediction of Water Quality by Using Artificial Intelligence,” Sustainability, vol. 13, no. 8, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Aaron Cardenas-Martinez et al., “Predictive Modelling Benchmark of Nitrate Vulnerable Zones at a Regional Scale Based on Machine Learning and Remote Sensing,” Journal of Hydrology, vol. 603, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Raheleh Arabgol, Majid Sartaj, and Keyvan Asghari, “Predicting Nitrate Concentration and its Spatial Distribution in Groundwater Resources Using Support Vector Machines (SVMs) Model,” Environmental Modeling & Assessment, vol. 21, pp. 71-82, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[22] AH Zare, VM Bayat, and A.P. Daneshkare, “Forecasting Nitrate Concentration in Groundwater Using Artificial Neural Network and Linear Regression Models,” International Agrophysics, vol. 25, no. 2, pp. 187-192, 2011.
[Google Scholar] [Publisher Link]
[23] Hanan Darwishe et al., “Prediction and Control of Nitrate Concentrations in Groundwater by Implementing a Model based on GIS and Artificial Neural Networks (ANN),” Environmental Earth Sciences, vol. 76, pp. 1-14, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Farzaneh Sajedi-Hosseini et al., “A Novel Machine Learning-Based Approach for the Risk Assessment of Nitrate Groundwater Contamination,” Science of the Total Environment, vol. 644, pp. 954-962, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Abobakr Saeed Abobakr Yahya et al., “Water Quality Prediction Model based Support Vector Machine Model for Ungauged River Catchment under Dual Scenarios,” Water, vol. 11, no. 6, pp. 1-16, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[26] M. Ehteshami, N. Dolatabadi Farahani, and S. Tavassoli, “Simulation of Nitrate Contamination in Groundwater Using Artificial Neural Networks,” Modeling Earth Systems and Environment, vol. 2, pp. 1-10, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Seiyed Mossa Hosseini, and Najmeh Mahjouri, “Developing a Fuzzy Neural Network-Based Support Vector Regression (FNN-SVR) for Regionalizing Nitrate Concentration in Groundwater,” Environmental Monitoring and Assessment, vol. 186, pp. 3685-3699, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Vasant Madhav Wagh, Dipak Baburao Panaskar, and Aniket Avinash Muley, “Estimation of Nitrate Concentration in Groundwater of Kadava River Basin-Nashik District, Maharashtra, India by Using Artificial Neural Network Model,” Modeling Earth Systems and Environment, vol. 3, pp. 1-10, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Atefeh Nouraki et al., “Prediction of Water Quality Parameters Using Machine Learning Models: A Case Study of the Karun River, Iran,” Environmental Science and Pollution Research, vol. 28, pp. 57060-57072, 2021.
[CrossRef] [Google Scholar] [Publisher Link]