ArticleScientific reports2026
Automated machine learning achieves accurate water quality prediction with reduced parameter requirements.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- 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
- Predicting metabolic syndrome in adolescents from device-measured 24-h movement behaviors: a machine learning approach.Frontiers in public health · 2026Article
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
Abstract
Accurate water quality assessment is critical for environmental monitoring and public health. Conventional Water Quality Index (WQI) computation methods, however, often rely on numerous parameters and labor-intensive processes, thus limiting their practicality for rapid assessments. While Machine Learning (ML) offers promising alternatives, the development of high-performing models typically demands extensive expertise and computational resources. This study addresses the latter gap by leveraging Automated Machine Learning (AutoML), specifically the AutoGluon platform, to predict WQI from a reduced set of readily available water quality parameters. Our objectives were to (i) evaluate the predictive performance of AutoML with reduced inputs, (ii) assess model interpretability via feature importance, and (iii) propose an automated framework for efficient water quality monitoring. This study analyzed a 36-year dataset from Taiwan's national river water quality monitoring network, focusing on four parameters: electrical conductivity (EC), suspended solids (SS), water temperature (WT), and pH. A fully automated pipeline handled model selection, hyperparameter tuning, and ensemble construction by systematically testing multiple algorithm families and stacking strategies to determine the optimal setup for each parameter. This eliminated manual intervention and delivered reproducible, data-driven results that matched the distinct spatiotemporal patterns in the long-term records. Among the evaluated algorithms, ensemble-based tree models (CatBoost, Random Forest, and XGBoost) demonstrated superior performance, achieving a mean [Formula: see text] of 0.76 and low predictive errors. Feature importance analysis identified EC as the most influential predictor. Such results highlight the feasibility of reducing the number of input parameters without compromising prediction accuracy, thereby enabling faster and cost-effective water quality assessments.
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.