AI-Driven Water Quality Index Prediction Framework for River Monitoring in India: Modeling, Explainability, and Policy Implications

DOI

10.2991/978-94-6463-948-3_12How to use a DOI?

Keywords

Water Quality Index; Machine Learning; XGBoost; SHAP; River; Monitoring; India; Environmental Policy; Explainable AI

Abstract

Proper evaluation of river water quality is the key to the sustenance of the eco-system, sustainable use of resources, and the preservation of human health. In the current work, we will present an artificial intelligence-based algorithm that will predict the Water quality index (WQI) of Indian rivers using ma- chine learning algorithms, namely, Linear Regression, Random Forest, and eX- treme Gradient Boosting (XGBoost). The data set has integrated a large amount of physicochemical and weathering variables, i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, nitrates, temperature, and rainfall. Experimental analysis shows that XGBoost yields the optimum predictive results (R 2 = 0.89, RMSE = 5.1, MAE = 3.7), which is greater than baseline and ensemble choices. In order to improve the interpretability, we use SHAP (SHapley Additive exPlanations) analysis that selects BOD, COD, and DO as the most significant parameters that influence the prediction of WQI. Moreover, case studies on urban-rural river reaches show that the model is able to model seasonal changes, pollu-tion peaks and festival-related unique features. The results highlight the value of explainable AI in aiding the active surveillance of rivers to facilitate early alerts and make decisions regarding policy changes. Future directions that involve incorporation of real time sensor information, prediction of wastewater pollution, and climate resilience modeling have also been outlined in the study, thus playing a role in sustainable water governance and SDG 6 (Clean Water and Sanitation).

Copyright

© 2025 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  - Maya Kurulekar
AU  - Mohit Sapat
AU  - Richa Panchgaur
PY  - 2026
DA  - 2026/01/06
TI  - AI-Driven Water Quality Index Prediction Framework for River Monitoring in India: Modeling, Explainability, and Policy Implications
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 182
EP  - 194
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_12
DO  - 10.2991/978-94-6463-948-3_12
ID  - Kurulekar2026
ER  -