ArticleSensors (Basel, Switzerland)2020
Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 94 citations in OpenAlex.
- Spatio-temporal patterns of land use and land cover, and their impact on groundwater quality in the industrialized Muvattupuzha basin.Scientific reports · 2025Article
- Harnessing machine learning for assessing climate change influences on groundwater resources: A comprehensive review.Heliyon · 2024Review
- Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern.Environmental science and pollution research international · 2024Article
- An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls.Heliyon · 2024Article
- Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia.ACS environmental Au · 2022Article
- Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches.Environmental science and pollution research international · 2022Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 7 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R
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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.