Smart Water Reuse System: A Multi-Stage Water Purification Approach with Life-Style Impact Prediction Using Machine Learning

Sukhavasi Saranya; Davuluri Hima Bindu; Juvva Viswa Tej; Vemuluri Kalyan Sai Ram; Kalamraju Abhinav; Phani Prasanthi

doi:10.2991/978-94-6463-940-7_37

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Smart Water Reuse System: A Multi-Stage Water Purification Approach with Life-Style Impact Prediction Using Machine Learning

Authors

Sukhavasi Saranya¹, Davuluri Hima Bindu¹, Juvva Viswa Tej¹, Vemuluri Kalyan Sai Ram², Kalamraju Abhinav³, Phani Prasanthi⁴^{, *}

¹UG students, Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India

²UG students, Department of Mechanical Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India

³UG students, Department of Information Technology, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India

⁴Professor, Department of Mechanical Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India

^*Corresponding author. Email: phaniprasanthi.parvathaneni@gmail.com

Corresponding Author

Phani Prasanthi

Available Online 31 December 2025.

DOI: 10.2991/978-94-6463-940-7_37 How to use a DOI?
Keywords: Water Reuse; Sustainable Water Management; Domestic Wastewater Purification; Multi-Stage Filtration
Abstract: In order to improve water quality and encourage sustainable water usage, this study provides the design, development, and performance assessment of a unique water filtration system. In order to provide safe and clean water for household and communal uses, the designed device has an effective filtration mechanism that can remove suspended particles, pollutants, and bacteria. Significant improvements in water quality measures, such as turbidity reduction and microbiological removal efficiency, were shown by experimental testing. In order to forecast possible water savings based on usage trends and purifying effectiveness, the system was additionally integrated with machine learning (ML) models. Gradient Boosting was the most accurate of the three studied machine learning techniques (Random Forest, Regression, and Gradient Boost), allowing for accurate prediction of water conservation potential. Depending on the number of homes (N), per-house water use (W), greywater fraction (g), adoption rate (a), and device efficiency (e), the machine learning predictions using the Gradient Boosting approach showed that the device might save up to 500–3,000 liters/day under normal operating conditions. The system’s dual capability of guaranteeing a clean water supply and offering predictive insights for sustainable water management is highlighted by the combined experimental and computational results. This method combines data-driven conservation planning with a scalable water purifying technology.
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 Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
Series: Advances in Intelligent Systems Research
Publication Date: 31 December 2025
ISBN: 978-94-6463-940-7
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-940-7_37 How to use a DOI?
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

ris enw bib

TY  - CONF
AU  - Sukhavasi Saranya
AU  - Davuluri Hima Bindu
AU  - Juvva Viswa Tej
AU  - Vemuluri Kalyan Sai Ram
AU  - Kalamraju Abhinav
AU  - Phani Prasanthi
PY  - 2025
DA  - 2025/12/31
TI  - Smart Water Reuse System: A Multi-Stage Water Purification Approach with Life-Style Impact Prediction Using Machine Learning
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 511
EP  - 521
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_37
DO  - 10.2991/978-94-6463-940-7_37
ID  - Saranya2025
ER  -

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