ArticleNature water2024
Deep learning for water quality.
Article in Nature water, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Advancing Near-Term Water Quality Forecasting with Explainable Machine Learning and Probabilistic Uncertainty Quantification.ACS ES&T water · 2026Article
- Explainable Stacked Ensemble Learning for Predicting Antibiotic Residues in the Danube River Within the Territory of the City of Novi Sad, Serbia.Antibiotics (Basel, Switzerland) · 2026Article
- Deep forecasting architectures mirror ecological time scales in water quality prediction.Environmental science and ecotechnology · 2026Article
- Hyperparameter-optimized deep learning for next-day pH forecasting in distributed water quality monitoring systems.Scientific reports · 2026Article
- Iodine-mediated proton-coupled electron transfer enables selective polymerization of organic pollutants in an oxidant-free electrocatalytic system.Nature communications · 2026Article
- Random Attention Mechanism for Deep Quality Modeling and Online Prediction in Chemical Processes.ACS omega · 2026Article
- Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.Environmental science & technology · 2026Review
- A Community Dataset for Large-Scale River Nitrogen Modeling in the United States.Scientific data · 2026Article
- Post-wildfire water quality and aquatic ecosystem response in the U.S. Pacific Northwest: science and monitoring gaps.Environmental research. Water · 2026Article
- Pathway-driven assessment of wastewater contamination in drinking water systems: integrating AI with public health risk.Frontiers in public health · 2026Review
- Riverine heat waves on the rise, outpacing air heat waves.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Time series forecasting of chlorophyll-a concentrations in the Chesapeake Bay.Scientific reports · 2025Article
- Deep Learning Prediction and Interpretation of Riverine Nitrate Export Across the Mississippi River Basin.Water resources research · 2025Article
- Rapid identification of Litopenaeus vannamei pathogenic bacteria: a combined approach using surface-enhanced Raman spectroscopy (SERS) and deep learning.Analytical and bioanalytical chemistry · 2025Article
- A multi-task deep neural network reveals inflowing river impacts for predictive lake management.Environmental science and ecotechnology · 2025Article
- Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction.Scientific reports · 2025Article
- High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023.Scientific data · 2025Article
- Groundwater quality evolution across China.Nature communications · 2025Article
- Leveraging the water-environment-health nexus to characterize sustainable water purification solutions.Nature communications · 2025Article
- Enhancing prediction and inference of daily in-stream nutrient and sediment concentrations using an extreme gradient boosting based water quality estimation tool - XGBest.The Science of the total environment · 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
5 authors.
Funding
Abstract
Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.
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.