ReviewHeliyon2024
Advances in machine learning and IoT for water quality monitoring: A comprehensive review.
Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 174 citations in OpenAlex.
- Citizen Science for Water Quality Monitoring: Analyzing Key Parameters, Success Factors, and Research Gaps for Aquaculture.Environmental management · 2025Pooled it
- Quantum dot sensors for heavy metal detection: from mechanistic design to field deployment: a review.RSC advances · 2026Review
- Effect-Directed Toxicity Assessment of the Surface Water of Chao Phraya River by Integrating a Panel of BioluminescentACS ES&T water · 2026Article
- Predictive modeling of heavy metal pollution and ecological risk for sustainable water quality management in the NY-NJ harbor system.Environmental monitoring and assessment · 2026Article
- From clay pots to graphene membranes: the evolutionary journey of water purification media.RSC advances · 2026Review
- PI-HydroGNN: a physics-informed spatiotemporal graph neural network framework for hydraulic reliability, leakage detection, and energy-efficient operation in water distribution systems.Scientific reports · 2026Article
- Meteorological Influence on Drinking Water Quality: Microbial Variability in Groundwater Wells and Piped Distribution Networks from Western Romania.Microorganisms · 2026Article
- High-performance low-velocity turbulent flow piezoelectric energy harvester using porous BaiScience · 2025Article
- Seasonal variations in microbial and physicochemical parameters of Nile river water in Giza, Egypt: comparison of IDEXX and conventional methods.BMC biotechnology · 2025Article
- Development of a Cost-Effective Multiparametric Probe for Continuous Real-Time Monitoring of Aquatic Environments.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning on Low-Cost Edge Devices for Real-Time Water Quality Prediction in Tilapia Aquaculture.Sensors (Basel, Switzerland) · 2025Article
- Adaptive power-saving mode control in NB-IoT networks using soft actor-critic reinforcement learning for optimal power management.Scientific reports · 2025Article
- The Integration of IoT (Internet of Things) Sensors and Location-Based Services for Water Quality Monitoring: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2025Review
- Article
- Structural insights into Beclin 1 interactions with it's regulators for autophagy modulation.Computational and structural biotechnology journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.