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Iranian association of environmental health (IAEH)
Avicenna J Environ Health Eng
. 2017;4(1): 11792. doi:
10.5812/ajehe.11792
Research Article
A Comparison of Performance of Artificial Neural Networks for Prediction of Heavy Metals Concentration in Groundwater Resources of Toyserkan Plain
Meysam Alizamir
1
* , Soheil Sobhanardakani
2
Cited by CrossRef: 9
1- Alizamir M, Sobhanardakani S. An Artificial Neural Network - Particle Swarm Optimization (ANN- PSO) Approach to Predict Heavy Metals Contamination in Groundwater Resources.
Jundishapur J Health Sci
. 2018;10(2)
[Crossref]
2- Huang P, Yang Z, Wang X, Ding F. Research on Piper-PCA-Bayes-LOOCV discrimination model of water inrush source in mines.
Arab J Geosci
. 2019;12(11)
[Crossref]
3- Maurya B, Yadav N, T A, J S, A S, V P, Iyer M, Yadav M, Vellingiri B. Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges.
Chemosphere
. 2024;353:141474
[Crossref]
4- Li Q, Fan G, Zhang D, Yu W, Zhang S, Fan Z, Fu Y. Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study.
Processes
. 2024;12(3):438
[Crossref]
5- Shokoohi R, Khazaei M, Mostafaloo R, Khazaei S, Signes-Pastor A, Ghahramani E, Torkshavand Z. Systematic review and meta-analysis of arsenic concentration in drinking water sources of Iran.
Environ Geochem Health
. 2024;46(5)
[Crossref]
6- Sihag P, Keshavarzi A, Kumar V. Comparison of different approaches for modeling of heavy metal estimations.
SN Appl Sci
. 2019;1(7)
[Crossref]
7- Ghobadi A, Cheraghi M, Sobhanardakani S, Lorestani B, Merrikhpour H. Hydrogeochemical characteristics, temporal, and spatial variations for evaluation of groundwater quality of Hamedan–Bahar Plain as a major agricultural region, West of Iran.
Environ Earth Sci
. 2020;79(18)
[Crossref]
8- Agbasi J, Egbueri J. Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review.
Environ Sci Pollut Res
. 2024;
[Crossref]
9- Agbasi J, Egbueri J. Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study.
J Sediment Environ
. 2023;8(1):57
[Crossref]
10- Huang P, Wang X. Piper-PCA-Fisher Recognition Model of Water Inrush Source: A Case Study of the Jiaozuo Mining Area.
Geofluids
. 2018;2018:1
[Crossref]
11- Egbueri J, Agbasi J. Data-driven soft computing modeling of groundwater quality parameters in southeast Nigeria: comparing the performances of different algorithms.
Environ Sci Pollut Res
. 2022;29(25):38346
[Crossref]
12- Wei J, Li G, Xie D, Yu G, Man X, Wang J. Discrimination of mine water-inflow sources based on the multivariate mixed model and fuzzy comprehensive evaluation.
Arab J Geosci
. 2020;13(17)
[Crossref]
13- Ghobadi A, Cheraghi M, Sobhanardakani S, Lorestani B, Merrikhpour H. Groundwater quality modeling using a novel hybrid data-intelligence model based on gray wolf optimization algorithm and multi-layer perceptron artificial neural network: a case study in Asadabad Plain, Hamedan, Iran.
Environ Sci Pollut Res
. 2022;29(6):8716
[Crossref]