Predicting arsenic and heavy metals contamination in groundwater resources of Ghahavand plain based on an artificial neural network optimized by imperialist competitive algorithm

Alizamir, Meysam; Sobhanardakani, Soheil

doi:10.15171/ehem.2017.31

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…These methods provide a valid tool for estimating heavy metal contents in soil near mining and suburban regions, thus facilitating the management and assignment of human settlements and other natural resources. Previous studies have reported that statistical methods and artificial neural networks can be successfully applied for estimating various other heavy metal contents in soils in various regions [34][35][36][37]. In this study, four popular and dominant soft computing techniques were used for the estimation of heavy metals in soil.…”

Section: Discussionmentioning

confidence: 99%

Comparison of different approaches for modeling of heavy metal estimations

2019

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Soil contamination by heavy metals is very important for environmental scientists due to the greater mobilization of metals and the possibility of contamination of groundwater. One of the most effective tools for evaluating the risk is the combination of experimentation with computer modeling. Modeling techniques are important in assessing the potential risks associated with heavy metals in the environment. Determination of models that can precisely evaluate the heavy metals in soils is an important need of agricultural researches, which could eradicate the weaknesses in the measurement of heavy metals in soils. The purpose of the present study is to test and compare different models according to their suitability for describing the estimation of heavy metals. The models used in this study were multilayer perceptron neural network (MLP), M5 model tree (M5) and bagging approach (BM5P). The data from 164 sampling sites from Neyshabur and Mashhad plains were taken in this study. The inputs combination according to feature selection-based correlation was used to feed the models. To model soil heavy metals, soil attributes, namely sand, silt, clay (as texture fractions), organic carbon, pH and available phosphorus, were entered in some models. To evaluate the performance of various techniques used in this study, several statistical indexes, including the correlation coefficient, root-mean-square error, Nash-Sutcliffe coefficient, Willmott's index (d) and mean absolute error, were assessed. Comparison of different models for Fe, Cu, Mn and Zn indicated that MLP is the most suitable method for estimations of Fe and Mn, whereas BM5P and M5P are the most suitable models for determinations of Cu and Zn, respectively. This study concluded that machine learning models can be successfully applied to the rapid prediction of soil heavy metals using soil variables.

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Section: Discussionmentioning

confidence: 99%

Comparison of different approaches for modeling of heavy metal estimations

2019

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“…An MLP network is composed of one or more input layers, one or more of hidden layers and an output layer [23]. Recently, ANNs have been widely and successfully used [22][23][24].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Particle swarm optimization of a neural network model for predicting the flashover voltage on polluted cap and pin insulator

Amel¹,

Yacine²,

Benguesmia³

2022

Diagnostyka

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This paper proposes training an artificial neural network (ANN) by a particle swarm optimization (PSO) technique to predict the flashover voltage of outdoor insulators. The analysis follows a series of real-world tests on high-voltage insulators to form a database for implementing artificial intelligence concepts. These tests are performed in various degrees of artificial contamination (distilled brine). Each contamination level shows the amount of contamination in milliliters per area of the isolator. The acquisition database provides values of flashover voltage corresponding to their electrical conductivity in each isolation zone and different degrees of artificial contamination. The results show that ANN trained by PSO can not only provide better prediction results, but also reduce the amount of computation efforts. It is also a more powerful model because: it does not get stuck in a local optimum. In addition, it also has the advantages of simple logic, simple implementation, and underlying intelligence. Compared to the results obtained by practical tests, the results obtained present that the PSO-ANN technique is very effective to predict flashover of high-voltage polluted insulators.

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“…An MLP network constitutes an input layer, one or such hidden layers, and an output layer (12). In recent years, ANNs are successfully used in environmental settings (13,31). In the current study, the Bayesian regularization algorithm was used to train ANN.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In this regard, only 2.8% of water on earth is fresh water; therefore, groundwater resources are highly valued due to their certain properties such as wide-spread occurrence and availability, and also good quality as an ideal supply of drinking water; due to these reasons more than half of the world's population depend on groundwater for survival (1)(2)(3)(4)(5)(6)(7). During the last few decades, contamination of groundwater resources is among the most important environmental issues due to hazardous chemical compounds such as heavy metals, pesticides, and petroleum hydrocarbons (8)(9)(10)(11)(12)(13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

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An Artificial Neural Network - Particle Swarm Optimization (ANN- PSO) Approach to Predict Heavy Metals Contamination in Groundwater Resources

Alizamir

Sobhanardakani

2018

Jundishapur J Health Sci

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Background: The quality of groundwater as the most important source for domestic, irrigation, and industrial purposes is affected by discharge of the chemicals from the anthropogenic resources. Therefore, the current study aimed at predicting heavy metals (As, Pb, Cu, and Zn) contamination in groundwater resources of Toyserkan Plain as an important agricultural area in Hamedan Province, West of Iran using artificial neural network -particle swarm optimization (ANN-PSO) approach. Methods: In the current study, samples were randomly selected from 20 groundwater wells with depth of 10 -90 m. The samples were filtered and kept cool in polyethylene bottles and then taken for the analysis of metal contents; they were acidified using nitric acid to reach pH < 2. Finally, element contents were determined using inductively coupled plasma -optical emission spectrometry (ICP-OES). Also, the performance of the PSO model was compared with that of ANN using Bayesian regulation (BR) training algorithm in terms of accuracy and model prediction efficiency.

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Predicting arsenic and heavy metals contamination in groundwater resources of Ghahavand plain based on an artificial neural network optimized by imperialist competitive algorithm

Cited by 14 publications

References 30 publications

Comparison of different approaches for modeling of heavy metal estimations

Comparison of different approaches for modeling of heavy metal estimations

Particle swarm optimization of a neural network model for predicting the flashover voltage on polluted cap and pin insulator

An Artificial Neural Network - Particle Swarm Optimization (ANN- PSO) Approach to Predict Heavy Metals Contamination in Groundwater Resources

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