Improving the accuracy of flow units prediction through two committee machine models: An example from the South Pars Gas Field, Persian Gulf Basin, Iran

Ghiasi-Freez, Javad; Kadkhodaie, Ali; Ziaii, Mansour

doi:10.1016/j.cageo.2012.04.006

Cited by 49 publications

(13 citation statements)

References 20 publications

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“…The degree of membership in fuzzy set is expressed in a closed unit interval [0, 1]. The exact values of 0 and 1 represent the total denial and affirmation of the membership, respectively (Ghiasi-Freez et al 2012). The main part of a fuzzy model is the fuzzy inference system (FIS) in which a given inputs is formulated to an output.…”

Section: Fuzzy Logicmentioning

confidence: 99%

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Barzegar

Moghaddam

Baghban

2015

Stoch Environ Res Risk Assess

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Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze NO À 3 concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the NO À 3 data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using NO À 3 concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and NO À 3 concentration values. After model training, the AI models are verified by the second NO À 3 concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.

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Section: Fuzzy Logicmentioning

confidence: 99%

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Barzegar

Moghaddam

Baghban

2015

Stoch Environ Res Risk Assess

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“…Therefore the performance of the CNN model could be better than any individual neural networks (Bagheripour 2014). Fundamental of committee machine networks were described by Bhatt and Helle (2002), Lim (2005) and Chen and Lin (2006), Kadkhodaie-Ilkhchi et al (2009) and Ghiasi-Freez et al (2012). The assumption is that, there are N trained ANNs with output vector O i , which are used to predict target vector T: The prediction error could be written as:…”

Section: Committee Neural Network (Cnn)mentioning

confidence: 99%

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Barzegar

Moghaddam

2016

Model. Earth Syst. Environ.

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Salinity is one of the main factors in groundwater quality monitoring. The main objectives of this study are to investigate and compare the accuracy of three different neural computing techniques, multi-layer perceptron neural network (MLP), radial basis function neural network (RBFNN), and generalized regression neural network (GRNN), in prediction of groundwater salinity of the Tabriz plain confined aquifer, expressed by electrical conductivity [EC (lS/cm)], and to employ an integrated method to combine the advantages of neural network models utilizing the concept of committee machine. To develop the models, 93 data records of groundwater samples were collected from East Azarbaijan regional water company. The data set including Ca 2? , Mg 2? , Na ? , SO 4 2-and Cl -concentrations as the inputs and salinity [EC (lS/ cm)] as an output were divided into two subsets; training and testing based on cross validation approach. After training and testing of the models, the performance of the models were evaluated using root mean square errors (RMSE), determination coefficient (R 2 ) and mean absolute error (MAE). The performance criteria of the constructed neural network models showed that RBFNN model has the best performance in predicting salinity. The committee neural network (CNN) combined the results of salinity predicted from MLP, RBFNN and GRNN, each of them has a weight factor showing its contribution in overall prediction. The optimal weights were derived by a genetic algorithm (GA). The results of salinity prediction derived from CNN showed that the CNN performs better than any one of the individual ANNs acting alone for predicting groundwater salinity.

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“…We model wax deposition by combining the results of two predictive models, i.e., SVR and ANN, through the CM concept. In CM, an ensemble of different models is run as a sophisticated, integrated, parallel approach to achieve more accuracy and generalization in the final prediction of wax deposition . GA is a tool for optimizing the performance of each element in the parallel structure of the CM.…”

Section: Theory: Committee Machine With Ann and Svrmentioning

confidence: 99%

Combining of intelligent models through committee machine for estimation of wax deposition

Gholami

Ansari²,

Ahmadi

2018

J Chinese Chemical Soc

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Deposition of the wax is one of the thorny issues in the petroleum industry, invoking costly problems during the transportation and production of crude oil. Owing to its devastating impacts on oil companies' economy, it is essential to develop a simple and robust strategy for the quantitative estimation of wax deposition. In this paper, support vector regression (SVR) is first proposed to estimate the amount of wax deposition. Subsequently, an artificial neural network (ANN) is developed for wax deposition prediction. Eventually, a sophisticated committee machine (CM) is constructed for combining the results of the SVR and ANN models. Optimal contribution of each model in final prediction of the wax deposit is determined through genetic algorithm in CM. Statistical error analysis shows that the CM model performs better than the individual models performing alone.

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Improving the accuracy of flow units prediction through two committee machine models: An example from the South Pars Gas Field, Persian Gulf Basin, Iran

Cited by 49 publications

References 20 publications

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction

Combining of intelligent models through committee machine for estimation of wax deposition

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