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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- Shi W, Huang J, Liu Y, Jing S, Zhou H, Li W, Wang Z, Zhang Z. Prediction of Spatiotemporal Pollution of Soil Heavy Metal in Mining Areas Based on Grey Neural Network Algorithm.
Water Air Soil Pollut
. 2024;235(12)
[Crossref]
4- 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]
5- Pirzad M, Sobhan Ardakani S. Qualitative modeling of groundwater resources using Artificial Neural Network and Gray Wolf Optimizer algorithm (Case Study: Kabudarahang Plain, Hamedan Province, Iran).
jehe
. 2023;11(1):29
[Crossref]
6- 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]
7- 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]
8- Sihag P, Keshavarzi A, Kumar V. Comparison of different approaches for modeling of heavy metal estimations.
SN Appl Sci
. 2019;1(7)
[Crossref]
9- 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]
10- 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;31(21):30370
[Crossref]
11- 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]
12- 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]
13- 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]
14- 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]
15- 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]