Estimation of the radon concentration in soil related to the environmental parameters by a modified Adaline neural network

Negarestani, Ali; Setayeshi, Saeid; Ghannadi‐Maragheh, Mohammad; Akashe, B

doi:10.1016/s0969-8043(02)00304-4

Cited by 35 publications

(14 citation statements)

References 12 publications

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“…Study on radon measurements in soil gas at the region that is 65 km N-NE of Chiang Mai City, Northern Thailand (Wattananikorn et al, 1998 A new method based on Adaptive Linear Neuron was developed by A. Negarestani and group for estimating radon concentration in soil (Negarestani et al, 2003). They performed a study at Thailand and they were able to differentiate the variation in radon concentration caused by environmental parameters and that caused by earthquake.…”

Section: Tablementioning

confidence: 99%

Anomalous radon emission as precursor of earthquake

Ghosh

Deb

Sengupta

2009

Journal of Applied Geophysics

197

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Section: Tablementioning

confidence: 99%

Anomalous radon emission as precursor of earthquake

Ghosh

Deb

Sengupta

2009

Journal of Applied Geophysics

197

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“…Due to the high background noise of radon time series, it is often impossible to distinguish an anomaly caused solely by a seismic event from one resulting from meteorological or hydrological parameters. For this reason, the implementation of more advanced statistical methods in data evaluation is important (Belyaev, 2001;Cuomo et al, 2000;Negarestani et al, 2003;Sikder & Munakata, 2009;Steinitz et al, 2003). In our research, radon has been monitored in several thermal springs (Gregorič et al, 2008;Zmazek et al, 2002a;Zmazek et al, 2006) and in soil gas (Zmazek et al, 2002b) and different approaches to distinguishing radon anomalies were applied.…”

Section: Methods For Detecting Anomalies In Radon Time Seriesmentioning

confidence: 98%

“…The aim is to identify radon anomalies which might be caused by seismic events. The application of artificial neural networks (Negarestani et al, 2002(Negarestani et al, , 2003Torkar et al, 2010), regression and model trees Sikder & Munakata, 2009;Zmazek et al, 2003;Zmazek et al, 2006) and some other methods (Sikder & Munakata, 2009;Steinitz et al, 2003) have proven to be useful means of extracting radon anomalies caused by seismic events.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Radon as an Earthquake Precursor – Methods for Detecting Anomalies

Gregorič¹,

Zmazek²,

Džeroski³

et al. 2012

Earthquake Research and Analysis - Statistical Studies, Observations and Planning

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“…The most used paradigms in artificial intelligence applications to nuclear scienceand particle physics are the expert system, general algorithms, fuzzy system, neural networks and hybrid system.Some applications to α, β, and  spectra have beenreported in the last decade. Further application ofANN in other cases where strong non-lineal effects arepresent like in the spectral analyses generated in analyticaltechniques like PIXE and XRF (X-ray fluorescence)are scarce and in BIXE spectra (beta induced X-rayemission) remain unexplored [14,21].…”

Section: Iintroductionmentioning

confidence: 99%

Artificial Neural Network: A tool for Rapid Quantitative Elemental Analysis Using Neutron Activation Analysis

2015

IJAREEIE

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An artificial neural network (ANN) has been trained with real-sample NAA (neutron activation analysis) spectra of irradiated materials. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations by NAA method. Results obtained with the ANN method are in good agreement with results obtained from NAA techniques, showing the high potentiality of ANN in NAA quantitative analyses. KEYWORDS:Artificial Neural Network, Neutron. I.INTRODUCTIONDevelopments from artificial intelligence like artificialneural networks, ANN, can be regarded as an engineeringprocedure emulating the human brain activity. It can bedefined as a set of non-linear and non-stationary interconnectionof elemental processes able to carry out at least oneof the following functions: training, remembrance and generalization,or, abstraction of substantial properties. Thefundamentals of the ANN technique have been described elsewhere[1-2] and several commercial and free codes area accessible nowadays.Applications of ANN to atomic and nuclear physics hasincreased during the last two decades [3][4][5][6][7][8][9][10][11][12][13], mainly inproblems related to nuclear reactors. The most used paradigms in artificial intelligence applications to nuclear scienceand particle physics are the expert system, general algorithms, fuzzy system, neural networks and hybrid system.Some applications to α, β, and  spectra have beenreported in the last decade. Further application ofANN in other cases where strong non-lineal effects arepresent like in the spectral analyses generated in analyticaltechniques like PIXE and XRF (X-ray fluorescence)are scarce and in BIXE spectra (beta induced X-rayemission) remain unexplored [14,21].Application of ANN is found in several issues where similar patterns have been represented by many spectra with specific differences. A typical case is presented in the elemental analysis of samples of unknown composition by neutron activation analysis (NAA) technique. The corresponding spectra represent the peaks containing known energy with variable intensities holding necessary information for the evaluation of the primary concentrations present in each sample. NAAis a nuclear technique based on the detection of -rays emitted by radionuclides neutrons [2][3][4][5]. Activation analysis differs from the other methods such as chemical analysis; it depends on the properties of the nucleus and not on the behavior of electrons in the outer shells of the atom. In general, NAA has many advantages over many conventional methods such as simultaneous analysis of multi-component samples and high degree of sensitivity for the majority of elements, many elements can be determined at levels of parts per billion.The basic essentials required to carry out analysis of samples by NAA are; source of neutrons, suitable instrumentation for detecting gamma rays, and a detailed knowledge of the reactions that occur when neutrons interact with target nuclei.One of the factors that define the quality of t...

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Estimation of the radon concentration in soil related to the environmental parameters by a modified Adaline neural network

Cited by 35 publications

References 12 publications

Anomalous radon emission as precursor of earthquake

Anomalous radon emission as precursor of earthquake

Radon as an Earthquake Precursor – Methods for Detecting Anomalies

Artificial Neural Network: A tool for Rapid Quantitative Elemental Analysis Using Neutron Activation Analysis

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