Water Quality Estimation Through Machine Learning Multivariate Analysis

2025-12-02

AI Summary

This study addresses the challenge of rapid, regulatory-compliant water quality assessment in agriculture and food processing industries. We propose a multivariate analytical method integrating ultraviolet–visible (UV-Vis) spectroscopy with machine learning. Specifically, we develop regression models to accurately predict key water quality parameters—including chemical oxygen demand (COD), nitrate (NO₃⁻) concentration, and turbidity—thereby significantly improving both detection efficiency and accuracy. To enhance model interpretability and decision trustworthiness, we innovatively incorporate the SHapley Additive exPlanations (SHAP) framework to quantitatively attribute predictive contributions to individual wavelength absorbances. The method requires no complex sample pretreatment or chemical reagents, offering advantages of low cost, non-destructiveness, and real-time capability. By enabling rapid, reagent-free spectral analysis coupled with interpretable modeling, our approach establishes a scalable, digital-ready paradigm for intelligent water resource monitoring and regulatory compliance.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The quality of water is key for the quality of agrifood sector. Water is used in agriculture for fertigation, for animal husbandry, and in the agrifood processing industry. In the context of the progressive digitalization of this sector, the automatic assessment of the quality of water is thus becoming an important asset. In this work, we present the integration of Ultraviolet-Visible (UV-Vis) spectroscopy with Machine Learning in the context of water quality assessment aiming at ensuring water safety and the compliance of water regulation. Furthermore, we emphasize the importance of model interpretability by employing SHapley Additive exPlanations (SHAP) to understand the contribution of absorbance at different wavelengths to the predictions. Our approach demonstrates the potential for rapid, accurate, and interpretable assessment of key water quality parameters.

Problem

• Estimating water quality using UV-Vis spectroscopy and machine learning
• Ensuring water safety and regulatory compliance through automated assessment
• Providing interpretable predictions with SHAP for key water parameters

Innovation

• Integrating UV-Vis spectroscopy with machine learning
• Using SHAP for interpretable model analysis
• Enabling rapid, accurate water quality assessment