Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Prediction of Physico-Chemical Characteristics of Groundwater Using Machine Learning Model

Authors
L. Bhagya Lakshmi1, P. Ramakoteswara Rao2, Ch. Chandra Mohan3, Lella Kranthi Kumar4, Kusuma Sundara Kumar5, *, Bandaru Venkata Shiva Kumar6
1Sr.Asst.Professor, Freshman Engineering Department, Lakireddy Bali Reddy College of Engineering, Mylavaram, 521230, India
2Asst. Professor, Freshman Engineering Department, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada.A.P, India
3Asst. Professor, CSIT Department, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, A.P, India
4Asst. Professor, School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
5Professor, Dept. of R &D, Bonam Venkata Chalamayya Engineering College-Odalarevu Konaseema, Amalapuram, Andhra Pradesh, India
6Professor, Department of Civil Engineering, WISTM Engineering College, Pendurthi, Visakhapatnam, Andhra Pradesh, India, 531173
*Corresponding author. Email: skkusuma123@gmail.com
Corresponding Author
Kusuma Sundara Kumar
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_60How to use a DOI?
Keywords
Ground water; Physico-chemical characteristics; Deep learning; EWQI; prediction
Abstract

— To maintain future supplies of clean drinking water, it is necessary to assess the state and degree of contamination in current groundwater. Predicting water quality properly is critical for reducing pollution and improving water management. This research offers a deep learning (DL)-based algorithm for predicting groundwater quality. To calculate the entropy weight-based groundwater quality index (EWQI), 200 groundwater samples are gathered from the research region, which is mostly utilized for agriculture in Krishna district, Andhra Pradesh, India. A variety of physicochemical characteristics are assessed in these samples. A set of five error metrics were created for assessing the performance of the model. The results show that the DL model, is working well with R2value of 0.996. It was shown that the most realistic and accurate method for predicting groundwater quality was the DL model.

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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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
10.2991/978-94-6463-471-6_60
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_60How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - L. Bhagya Lakshmi
AU  - P. Ramakoteswara Rao
AU  - Ch. Chandra Mohan
AU  - Lella Kranthi Kumar
AU  - Kusuma Sundara Kumar
AU  - Bandaru Venkata Shiva Kumar
PY  - 2024
DA  - 2024/07/30
TI  - Prediction of Physico-Chemical Characteristics of Groundwater Using Machine Learning Model
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 618
EP  - 628
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_60
DO  - 10.2991/978-94-6463-471-6_60
ID  - Lakshmi2024
ER  -