Data-Driven Monitoring for Thermal Water Quality Control: Anomaly Detection from Predictive Forecasting in the AQUAPRED Project

Abstract

[Abstract]: To control the quality of mineral-medicinal waters and ensure their therapeutic benefits, spas often rely on periodic discrete sampling to analyze the physico-chemical properties of their pools. The AQUAPRED project aims to digitize this process by deploying IoT systems within the spa facilities, enabling real-time data acquisition via calibrated multi-parameter probes. Using data collected by these pilot systems, we develop and validate a predictive machine learning model capable of forecasting the short-term evolution of the thermal water properties. Historical data from each facility allow the model to learn the specifics dynamics of each spa. As a practical application, we propose an anomaly detection module based on residual analysis from predicted and observed values. Significant discrepancies signal events of interest and emergent trends, such as anomalous readings, contamination or sensor drift. The methodology is evaluated using real data from six spas associated with the AQUAPRED project. The results demonstrate the model’s effectiveness and support its feasibility for deployment in other thermal establishments.