ReviewEco-Environment & Health2022
A review of the application of machine learning in water quality evaluation.
Review in Eco-Environment & Health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 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
50 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent Implementations of Hydrogel-Based Microbial Electrochemical Technologies (METs) in Sensing Applications.Sensors (Basel, Switzerland) · 2023Pooled it
- Advancing Near-Term Water Quality Forecasting with Explainable Machine Learning and Probabilistic Uncertainty Quantification.ACS 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
- Metaheuristic-optimized interaction-aware deep learning with large language model assistance for data-driven water quality prediction.Scientific reports · 2026Article
- Interpretable machine learning for river salinity dynamics in arid basins.Scientific reports · 2026Article
- Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.Environmental science & technology · 2026Review
- Enhancing reservoir water quality simulation through machine learning-driven remote sensing integration with EFDC: a coupled framework for eutrophication management in data-scarce regions.Scientific reports · 2026Article
- Source identification of sudden water pollution events in the Dongliao River using a hybrid machine learning framework.Scientific reports · 2026Article
- Rising Global Riverine Deoxygenation Rates and GHG Emissions Driven by the Synergistic Effects of Warming and Anthropogenic Land Use Expansion.Global change biology · 2026Article
- Application of the Two-Layer Regularized Gated Recurrent Unit (TLR-GRU) Model Enhanced by Sliding Window Features in Water Quality Parameter Prediction.Entropy (Basel, Switzerland) · 2026Article
- Determination of biochemical and chemical oxygen demand in wastewater using UV-Vis and EEM spectroscopy coupled with chemometrics.Analytical and bioanalytical chemistry · 2026Article
- Unlocking Soil Health and Surface Water Quality Management: A Review on Fluorescent Dissolved Organic Matter (fDOM) in Agricultural Systems.Journal of agricultural and food chemistry · 2026Review
- A hybrid transformer-BiLSTM model optimized with Firefly Algorithm for network traffic anomaly detection.PloS one · 2026Article
- Pathway-driven assessment of wastewater contamination in drinking water systems: integrating AI with public health risk.Frontiers in public health · 2026Review
- Prediction of Water Quality Parameters in the Paraopeba River Basin Using Remote Sensing Products and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Review
- Integrated groundwater quality assessment using geochemical modelling and machine learning approach in Northern India.Scientific reports · 2025Article
- Developing highly accurate machine learning models for optimizing water quality management decisions in tilapia aquaculture.Scientific reports · 2025Article
- Convolutional neural networks with transfer learning for natural river flow prediction in ungauged basins.Scientific reports · 2025Article
- A SMOTE PCA HDBSCAN approach for enhancing water quality classification in imbalanced datasets.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.
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.