A Machine Learning Analysis of Groundwater Heavy Metals Contamination

DOI

10.2991/978-94-6463-250-7_9How to use a DOI?

Keywords

Machine learning; Groundwater contamination; Decision Tree regression method; Heavy metals

Abstract

This project’s objective is to classify as safe and unsafe levels of heavy metals using machine learning. In the southern state of Tamil Nadu, samples of groundwater collected from the Arani Taluk were used to generate these levels. An integrated machine-learning framework was developed during this study for the detection of groundwater contamination. In the Arani Taluk of Tamil Nadu, we collected forty-four samples of groundwater, and to characterize and evaluate the water quality, heavy metals were determined. As a result of seasonal variations, the quality of the water was determined. Trace metals such [11] as Mn, Ni, Co, Fe, Cu, Zn, Pb, and Cr were analysed for the pre-monsoon and post-monsoon seasons. The amount of metal present in groundwater was predicted using machine learning algorithm (Decision Tree). Our goal also includes a comparison of the accuracy between the conventional method and the machine learning model-based method. We have also provided visual graphs and images in addition to the above two features in order to support this study. This is to provide a better understanding of this research.

Our main outcome of this study includes creating an efficient machine learning model to estimate the value of contaminants in a given area and a comparison between post-monsoon groundwater metals and pre-monsoon groundwater metals using the conventional and machine-learning method was successfully made.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Sankari
AU  - R. Subhashini
AU  - P. Mohana
PY  - 2023
DA  - 2023/10/17
TI  - A Machine Learning Analysis of Groundwater Heavy Metals Contamination
BT  - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023)
PB  - Atlantis Press
SP  - 42
EP  - 47
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-250-7_9
DO  - 10.2991/978-94-6463-250-7_9
ID  - Sankari2023
ER  -