Real-Time and Explainable Hybrid Machine Learning Framework for Multivariate Prediction and Classification of Water Contamination Using Environmental and Temporal Features

Er. Prafull Kothari, S. Sagaya Mary, Jyotsna Pandit, D. B. K. Kamesh, Priyadharshini K., Syed Hauider Abbas

2025

Abstract

Accurate prediction and categorization of water contamination is important to protect public health and promote sustainable water resource. In this research, we introduce a real-time, explainable hybrid supplemented machine learning approach, weaving deep learning and classical classifiers, as well as exploiting a broad variety of environmental and temporal features, to predict and categorize the pollution level of water. While other models are based on small datasets or do not have multimodal or reasoning capabilities, the proposed model uses a combined CNN–LSTM–XGBoost model with an explainability layer using SHAP values, in order to provide interpretable decisions. The model involves chemical parameters and context factors including meteorological data, seasonal pattern, and human activities to increase accuracy and generality among diverse water bodies. Real-time integration of sensors and quantification of uncertainty enhance the potential of the model to provide timely decision support and accurate alerts regarding contamination risk. This article presents a solution to next generation water quality intelligence systems which is both robust and scalable as well as interpretable.

Paper Citation

in Harvard Style

Kothari E., Mary S., Pandit J., Kamesh D., K. P. and Abbas S. (2025). Real-Time and Explainable Hybrid Machine Learning Framework for Multivariate Prediction and Classification of Water Contamination Using Environmental and Temporal Features. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 544-551. DOI: 10.5220/0013869000004919

in Bibtex Style

@conference{icrdicct`2525,
author={Er. Kothari and S. Mary and Jyotsna Pandit and D. Kamesh and Priyadharshini K. and Syed Abbas},
title={Real-Time and Explainable Hybrid Machine Learning Framework for Multivariate Prediction and Classification of Water Contamination Using Environmental and Temporal Features},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={544-551},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013869000004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Real-Time and Explainable Hybrid Machine Learning Framework for Multivariate Prediction and Classification of Water Contamination Using Environmental and Temporal Features
SN - 978-989-758-777-1
AU - Kothari E.
AU - Mary S.
AU - Pandit J.
AU - Kamesh D.
AU - K. P.
AU - Abbas S.
PY - 2025
SP - 544
EP - 551
DO - 10.5220/0013869000004919
PB - SciTePress