Machine Learning-Based Model for Predicting Bacteriological Contamination in Water

Simran Kaul; P. Sughosh; S. Girisha; G. Savitha

doi:10.1007/978-981-95-2030-5_20

Machine Learning-Based Model for Predicting Bacteriological Contamination in Water

Simran Kaul
, P. Sughosh^*
, S. Girisha
, G. Savitha

^*Corresponding author for this work

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Predicting total coliform levels in water accurately is important for protecting public health and the environment. In this study, Support Vector Machines (SVM) and Decision Tree models are used to predict total coliforms in Indian surface and groundwater sources using physicochemical water quality parameters as input. A dataset was prepared by referring to various research articles in the Scopus database and the same was used for training, testing, and validation (70:15:15) of the model to ensure high quality performance. Both the models were assessed using error-based metrics such as R-squared (R²), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The Decision Tree Regressor outperformed SVM Regressor in predicting the total coliforms in the water samples with an R² value of 0.704 and 0.384, respectively. The study highlights the potential of Machine learning models in predicting water quality and thereby support effective monitoring and protection of water supply sources.

Original language	English
Title of host publication	Innovative Building Technologies - Select Proceedings of NHCE-ICEMS 2024
Editors	Wim Van den bergh, Subhash C. Yaragal, Shreelaxmi Prashanth, Poornachandra Pandit
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	263-272
Number of pages	10
ISBN (Print)	9789819520299
DOIs	https://doi.org/10.1007/978-981-95-2030-5_20
Publication status	Published - 2026
Event	International Conference on New Horizons in Civil Engineering- Innovative Civil Engineering Materials and Systems, NHCE-ICEMS 2024 - Manipal, India Duration: 12-12-2024 → 14-12-2024

Publication series

Name	Lecture Notes in Civil Engineering
Volume	752 LNCE
ISSN (Print)	2366-2557
ISSN (Electronic)	2366-2565

Conference

Conference	International Conference on New Horizons in Civil Engineering- Innovative Civil Engineering Materials and Systems, NHCE-ICEMS 2024
Country/Territory	India
City	Manipal
Period	12-12-24 → 14-12-24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 6 Clean Water and Sanitation

All Science Journal Classification (ASJC) codes

Civil and Structural Engineering

Access to Document

10.1007/978-981-95-2030-5_20

Cite this

Kaul, S., Sughosh, P., Girisha, S., & Savitha, G. (2026). Machine Learning-Based Model for Predicting Bacteriological Contamination in Water. In W. Van den bergh, S. C. Yaragal, S. Prashanth, & P. Pandit (Eds.), Innovative Building Technologies - Select Proceedings of NHCE-ICEMS 2024 (pp. 263-272). (Lecture Notes in Civil Engineering; Vol. 752 LNCE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-95-2030-5_20

Kaul, Simran ; Sughosh, P. ; Girisha, S. et al. / Machine Learning-Based Model for Predicting Bacteriological Contamination in Water. Innovative Building Technologies - Select Proceedings of NHCE-ICEMS 2024. editor / Wim Van den bergh ; Subhash C. Yaragal ; Shreelaxmi Prashanth ; Poornachandra Pandit. Springer Science and Business Media Deutschland GmbH, 2026. pp. 263-272 (Lecture Notes in Civil Engineering).

@inproceedings{1410e0987c0744fb8620284f0a403dd4,

title = "Machine Learning-Based Model for Predicting Bacteriological Contamination in Water",

abstract = "Predicting total coliform levels in water accurately is important for protecting public health and the environment. In this study, Support Vector Machines (SVM) and Decision Tree models are used to predict total coliforms in Indian surface and groundwater sources using physicochemical water quality parameters as input. A dataset was prepared by referring to various research articles in the Scopus database and the same was used for training, testing, and validation (70:15:15) of the model to ensure high quality performance. Both the models were assessed using error-based metrics such as R-squared (R2), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The Decision Tree Regressor outperformed SVM Regressor in predicting the total coliforms in the water samples with an R2 value of 0.704 and 0.384, respectively. The study highlights the potential of Machine learning models in predicting water quality and thereby support effective monitoring and protection of water supply sources.",

author = "Simran Kaul and P. Sughosh and S. Girisha and G. Savitha",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; International Conference on New Horizons in Civil Engineering- Innovative Civil Engineering Materials and Systems, NHCE-ICEMS 2024 ; Conference date: 12-12-2024 Through 14-12-2024",

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doi = "10.1007/978-981-95-2030-5\_20",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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Kaul, S, Sughosh, P, Girisha, S & Savitha, G 2026, Machine Learning-Based Model for Predicting Bacteriological Contamination in Water. in W Van den bergh, SC Yaragal, S Prashanth & P Pandit (eds), Innovative Building Technologies - Select Proceedings of NHCE-ICEMS 2024. Lecture Notes in Civil Engineering, vol. 752 LNCE, Springer Science and Business Media Deutschland GmbH, pp. 263-272, International Conference on New Horizons in Civil Engineering- Innovative Civil Engineering Materials and Systems, NHCE-ICEMS 2024, Manipal, India, 12-12-24. https://doi.org/10.1007/978-981-95-2030-5_20

Machine Learning-Based Model for Predicting Bacteriological Contamination in Water. / Kaul, Simran; Sughosh, P.; Girisha, S. et al.
Innovative Building Technologies - Select Proceedings of NHCE-ICEMS 2024. ed. / Wim Van den bergh; Subhash C. Yaragal; Shreelaxmi Prashanth; Poornachandra Pandit. Springer Science and Business Media Deutschland GmbH, 2026. p. 263-272 (Lecture Notes in Civil Engineering; Vol. 752 LNCE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Machine Learning-Based Model for Predicting Bacteriological Contamination in Water

AU - Kaul, Simran

AU - Sughosh, P.

AU - Girisha, S.

AU - Savitha, G.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

PY - 2026

Y1 - 2026

N2 - Predicting total coliform levels in water accurately is important for protecting public health and the environment. In this study, Support Vector Machines (SVM) and Decision Tree models are used to predict total coliforms in Indian surface and groundwater sources using physicochemical water quality parameters as input. A dataset was prepared by referring to various research articles in the Scopus database and the same was used for training, testing, and validation (70:15:15) of the model to ensure high quality performance. Both the models were assessed using error-based metrics such as R-squared (R2), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The Decision Tree Regressor outperformed SVM Regressor in predicting the total coliforms in the water samples with an R2 value of 0.704 and 0.384, respectively. The study highlights the potential of Machine learning models in predicting water quality and thereby support effective monitoring and protection of water supply sources.

AB - Predicting total coliform levels in water accurately is important for protecting public health and the environment. In this study, Support Vector Machines (SVM) and Decision Tree models are used to predict total coliforms in Indian surface and groundwater sources using physicochemical water quality parameters as input. A dataset was prepared by referring to various research articles in the Scopus database and the same was used for training, testing, and validation (70:15:15) of the model to ensure high quality performance. Both the models were assessed using error-based metrics such as R-squared (R2), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The Decision Tree Regressor outperformed SVM Regressor in predicting the total coliforms in the water samples with an R2 value of 0.704 and 0.384, respectively. The study highlights the potential of Machine learning models in predicting water quality and thereby support effective monitoring and protection of water supply sources.

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DO - 10.1007/978-981-95-2030-5_20

M3 - Conference contribution

AN - SCOPUS:105037576519

SN - 9789819520299

T3 - Lecture Notes in Civil Engineering

SP - 263

EP - 272

BT - Innovative Building Technologies - Select Proceedings of NHCE-ICEMS 2024

A2 - Van den bergh, Wim

A2 - Yaragal, Subhash C.

A2 - Prashanth, Shreelaxmi

A2 - Pandit, Poornachandra

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on New Horizons in Civil Engineering- Innovative Civil Engineering Materials and Systems, NHCE-ICEMS 2024

Y2 - 12 December 2024 through 14 December 2024

ER -

Kaul S, Sughosh P, Girisha S, Savitha G. Machine Learning-Based Model for Predicting Bacteriological Contamination in Water. In Van den bergh W, Yaragal SC, Prashanth S, Pandit P, editors, Innovative Building Technologies - Select Proceedings of NHCE-ICEMS 2024. Springer Science and Business Media Deutschland GmbH. 2026. p. 263-272. (Lecture Notes in Civil Engineering). doi: 10.1007/978-981-95-2030-5_20

Machine Learning-Based Model for Predicting Bacteriological Contamination in Water

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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