RT Journal Article
T1 A soft sensor open-source methodology for inexpensive monitoring of water quality: a case study of NO3− concentrations
A1 Chaves García, Antonio Jesús
A1 Martín-Fernández, Cristian
A1 Llopis-Torres, Luis Manuel
A1 Díaz-Rodríguez, Manuel
A1 Fernández-Ortega, Jaime
A1 Barberá-Fornell, Juan Antonio
A1 Andreo-Navarro, Bartolomé
K1 Detectores químicos
K1 Nitratos
AB Nitrate (NO3−) concentrations in aquifers constitute a global problem affecting environmental integrity and public health. Unfortunately, deploying hardware sensors specifically for NO3− measurements can be expensive, thereby, limiting scalability. This research explores the integration of soft sensors with data streams through an use case to predict nitrate NO3− levels in real time. To achieve this objective, a methodology based on Kafka-ML is proposed, a framework designed to manage the pipeline of machine learning models using data streams. The study evaluates the effectiveness of this methodology by applying it to a real-world scenario, including the integration of low-cost sensor devices. Additionally, Kafka-ML is extended by integrating MQTT and other IoT data protocols. The methodology benefits include rapid development, enhanced control, and visualisation of soft sensors. By seamlessly integrating IoT and data analytics, the approach promotes the adoption of cost-effective solutions for managing NO3− pollution and improving sustainable water resource monitoring.
PB Elsevier
YR 2025
FD 2025-01
LK https://hdl.handle.net/10630/36883
UL https://hdl.handle.net/10630/36883
LA eng
NO Antonio Jesús Chaves, Cristian Martín, Luis Llopis Torres, Manuel Díaz, Jaime Fernández-Ortega, Juan Antonio Barberá, Bartolomé Andreo, A soft sensor open-source methodology for inexpensive monitoring of water quality: A case study of NO3− concentrations, Journal of Computational Science, Volume 85, 2025, 102522, ISSN 1877-7503, https://doi.org/10.1016/j.jocs.2024.102522
NO This work is a contribution to the PRIMA funded European project KARMA (ANR-18-PRIM-0005); financed by the Ministry of Science and Innovation of Spain and FEDER funding inside the Operational Plurirregional Program of Spain 2014–2020 and the Operational Program of Smart Growing (Environmental and Biodiversity Climate Change Lab, EnBiC2-Lab; LIFEWATCH-2019-11-UMA-01-BD); funded by the project PCI2019-103675 of the International Joint Programme of the Ministry of Science, Innovation and Universities of Spain, the project P06-RNM 2161 funded by the Autonomous Government of Andalusia (Spain), the project PLSQ-00230 (‘iSAT: Sistema de Alerta Temprana Inteligente’) funded by the Autonomous Government of Andalusia (Spain) and to the Research Group RNM-308 funded by the Autonomous Government of Andalusia (Spain). This work is funded by the Spanish projects: Grant PID2022-141705OB-C21 (‘DiTaS: A framework for agnostic compositional and cognitive digital twin services’) funded by MICIU/AEI/10.13039/5011000110331 and by ‘FEDER ’; Grant TED2021-130167B (‘GEDIER: Application of Digital Twins to more sustainable irrigated farms’), funded by MICIU/AEI/10.13039/5011000110331 and by ‘European Union NextGenerationEU/PRTR’; Grant CPP2021-009032 (‘ZeroVision: Enabling Zero impact wastewater treatment through Computer Vision and Federated AI’) funded by MICIU/AEI/10.13039/5011000110331 and by ‘European Union NextGenerationEU/PRTR ’. Funding for open access charge: Universidad de Málaga / CBUA .
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 4 mar 2026