Water Quality Estimation Through Machine Learning Multivariate Analysis

TL;DR

This paper presents a machine learning approach combined with UV-Vis spectroscopy for rapid, accurate, and interpretable water quality assessment, crucial for ensuring safety and regulatory compliance in the agrifood sector.

Contribution

It introduces an integrated method using UV-Vis spectroscopy and machine learning with SHAP for interpretability, advancing automatic water quality evaluation.

Findings

Effective prediction of water quality parameters

Enhanced interpretability with SHAP explanations

Potential for rapid water safety assessments

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.