IoT and Machine Learning Applications for Water Quality Monitoring in Rural Communities of Emerging Countries: Systematic Review

Abstract

Monitoring water quality remains a persistent challenge in rural communities of emerging countries, where traditional methods are often costly, slow, and difficult to implement. In recent years, the integration of Internet of Things (IoT) technologies and Machine Learning (ML) techniques has emerged as an efficient alternative to improve the accuracy, continuity, and predictive capacity of water monitoring systems. This Systematic Literature Review aimed to identify and analyze the approaches, parameters, technological platforms, and models used in studies that combine IoT and ML for water quality monitoring in rural settings. A structured search was conducted exclusively in the Scopus database, resulting in the selection of 26 primary studies, which were analyzed using a PICOC-based extraction matrix. The results show that most investigations focus on rural environments and developing countries, with rivers being the most monitored water bodies and the Water Quality Index (WQI) the most frequently employed parameter. Additionally, fixed IoT nodes and ML models for predicting physicochemical variables were the most common technological solutions. Although these technologies show strong potential, the field still presents limitations, including a lack of standardized metrics and insufficient reporting of system costs and energy autonomy. Overall, the convergence of IoT and ML represents a promising pathway for strengthening water quality monitoring in rural communities, provided that future research improves methodological rigor and technical transparency.