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

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© 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.

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