ArticleHeliyon2024
Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 6 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
6 citing papers in PubMed.
- Seasonal water quality index prediction in Guilin karst water system: an explainable machine learning approach.Scientific reports · 2026Article
- Groundwater quality assessment for agricultural utilizing indexical and machine learning techniques in Ouled Djellal Aquifer, Southern Algeria.Scientific reports · 2026Article
- Examining the Acceptance and Use of AI-Based Assistive Technology Among University Students with Visual Disability: The Moderating Role of Physical Self-Esteem.Bioengineering (Basel, Switzerland) · 2025Article
- Air temperature estimation and modeling using data driven techniques based on best subset regression model in Egypt.Scientific reports · 2025Article
- Comparative evaluation of hybrid and individual models for predicting soybean yellow mosaic virus incidence.Scientific reports · 2025Article
- Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks.Scientific reports · 2024Article
Corrections and comments
- Retraction · 2026-03-19Concerns/Issues about Referencing/Attributions · Duplication of Data · Error in Text · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions ·
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO
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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.