Blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network

• Autorzy: Temeng Victor Amoako , Ziggah Yao Yevenyo , Arthur Clement Kweku

Identyfikatory

DOI

10.46873/2300-3960.1043

• Języki publikacji: EN

Abstrakty

EN

Although a major portion of the emitted energy from mine blast is sub-audible (lower frequency), there exist a component that is audible (high frequencies from 20 Hz to 20 KHz) and as such within the range of human hearing as noise. Unlike blast air overpressure (low frequency occurrence), noise prediction from mine blasting has received little scholarly attention in mining sciences. Noise from mine blast is considered a major detrimental blasting effect and can be a menace to nearby residents and workers in the mine. In this paper, a blast-induced noise level prediction model based on Brain Inspired Emotional Neural Network (BENN) is presented. The objective of this paper was to investigate the implementation possibility of the proposed BENN approach along with six other artificial intelligent methods, such as Backpropagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Generalised Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM) and Support Vector Machine (SVM). The study also implemented the standard Multiple Linear Regression (MLR) for comparison purposes. The statistical analysis carried out revealed that the BENN performed better than the other investigated methods. Thus, the BENN achieved very promising testing results of 1.619 dB, 3.076%, 0.0925%, 0.911 and 82.956% for root mean squared error (RMSE), mean absolute percentage error (MAPE), normalised root mean squared error (NRMSE), correlation coefficient (R) and variance accounted for (VAF). The implemented BENN can be useful in managing noise from mine blasting using site specific data.

Słowa kluczowe

EN

artificial intelligence; blast-induced noise level; emotional neural network; limbic system theory

PL

sztuczna inteligencja; poziom hałasu wywołanego wybuchem; sieć neuronowa; teoria układu limbicznego

• Wydawca: Elsevier ; Główny Instytut Górnictwa
• Czasopismo: Journal of Sustainable Mining
• Rocznik: 2021
• Tom: Vol. 20, iss. 1
• Strony: 28--38
• Opis fizyczny: Bibliogr. 57 poz.

Twórcy

autor

Temeng Victor Amoako

• Faculty of Mineral Resources Technology, Department of Mining Engineering, University of Mines and Technology, Tarkwa, Ghana

autor

Ziggah Yao Yevenyo

• Faculty of Mineral Resources Technology, Department of Geomatic Engineering, University of Mines and Technology, Tarkwa, Ghana

autor

Arthur Clement Kweku

• Faculty of Mineral Resources Technology, Department of Mining Engineering, University of Mines and Technology, Tarkwa, Ghana

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