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Novel Machine Learning Algorithms to Predict the Groundwater Vulnerability Index to Nitrate Pollution at Two Levels of Modeling by Hussam Eldin Elzain; Sang Yong Chung; Senapathi Venkatramanan; Sekar Selvam; Hamdi Abdurhman Ahemd; Young Kyo Seo; Md. Simul Bhuyan; Mohamed A. Yassin is a Environmental Science article available to read on EtoBox.

What is Novel Machine Learning Algorithms to Predict the Groundwater Vulnerability Index to Nitrate Pollution at Two Levels of Modeling about?

The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. In this research, novel intelligent predictive Machine Learning (ML) regression models of k-Neighborhood (KNN), ensemble Extremely Randomized Trees (ERT), and ensemble Bagging regression (BA) at two levels of modeling were utilized to improve DRASTIC-LU model in the Miryang aquifer located in South Korea. The predicted outputs from level 1 (KNN and ERT models) were used as inputs for ensemble bagging (BA) in level 2. The predictive groundwater pollution vulnerability index (GPVI), derived from DRASTIC-LU model was adjusted by NO3–N data and was utilized as the target data of the ML models. Hyperparameters for all models were tuned using a Grid Searching approach to determine the best effective model structures. Various statistical metrics and graphical representations were used to evaluate the superior predictive performance among ML models. Ensemble BA model in level 2 was more precise than standalone KNN and ensemble ERT models in level 1 for predicting GPVI values. Furthermore, the ensemble BA model offered suitable out

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Author

Hussam Eldin Elzain; Sang Yong Chung; Senapathi Venkatramanan; Sekar Selvam; Hamdi Abdurhman Ahemd; Young Kyo Seo; Md. Simul Bhuyan; Mohamed A. Yassin

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)