ArticleEnvironmental science and pollution research international2021
Prediction of water quality parameters using machine learning models: a case study of the Karun River, Iran.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Article
- Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition.Scientific reports · 2025Article
- Predictive modeling of climate change impacts using Artificial Intelligence: a review for equitable governance and sustainable outcome.Environmental science and pollution research international · 2025Review
- Article
- Application of hybrid improved temporal convolution network model in time series prediction of river water quality.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Accurate water quality predicting has an essential role in improving water management and pollution control. The machine learning models have been successfully implemented for modelling total dissolved solids (TDS), sodium absorption ratio (SAR) and total hardness (TH) content in aquatic ecosystems with insufficient data. However, due to multiple pollution sources and complex behaviours of pollutants, these models' effect in predicting TDS, SAR, and TH levels in the Karun River system is still unclear. Given this problem, multiple linear regression (MLR), M5P model tree, support vector regression (SVR) and random forest regression (RFR) models were used to predict TDS, SAR and TH variables in the four stations in the Karun River for 1999-2019 period. Initially, to reduce the number of input variables, the principal component analysis (PCA) technique was used. The developed models are valued in terms of the coefficient of determination (R
Indexed as
Identifiers
34081285What 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.