Soft computing-based technique as a predictive tool to estimate blast-induced ground vibration

Arthur, Clement Kweku; Temeng, Victor Amoako; Ziggah, Yao Yevenyo

doi:10.46873/2300-3960.1343

Cited by 3 publications

(3 citation statements)

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“…Various types of KFs have been used in various sources. In this study, based on practical experience of the leading problem and the results of others [47,48], six types of KF were used (Table 5). In this table, sl refers to the signal variance of function and length scale, respectively.…”

Section: Gaussian Process Regression (Gpr) Modelmentioning

confidence: 99%

The effect of EGR and hydrogen addition to natural gas on performance and exhaust emissions in a diesel engine by AVL fire multi-domain simulation, GPR model, and multi-objective genetic algorithm

Zareei

Abbas

Álvarez

2022

International Journal of Hydrogen Energy

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Section: Gaussian Process Regression (Gpr) Modelmentioning

confidence: 99%

The effect of EGR and hydrogen addition to natural gas on performance and exhaust emissions in a diesel engine by AVL fire multi-domain simulation, GPR model, and multi-objective genetic algorithm

Zareei

Abbas

Álvarez

2022

International Journal of Hydrogen Energy

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“…Other types of ANN applied in the prediction of blast-induced ground vibrations include GRNN, quantile regression neural network (QRNN), wavelet neural network (WNN), hybrid neural fuzzy inference system (HYFIS), adaptive neuro-fuzzy inference system (ANFIS), and group method of data handling (GMDH). Arthur et al [96] estimated blast-induced ground vibrations by comparing five ANNs (WNN, BPNN, RBFNN, GRNN, and GMDH) and four empirical models (Indian Standard, the United State Bureau of Mines, Ambrasey-Hendron, and Langefors and Kilhstrom). The study revealed that WNN with a single hidden layer and three wavelons produced highly satisfactory results compared to the benchmark methods of BPNN and RBFNN.…”

Section: Ground Vibrationmentioning

confidence: 99%

Advances in Computational Intelligence Applications in the Mining Industry

2022

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Company, a leading vertically integrated fertilizer producer with mines throughout the Americas and a global distribution network. From 2002 to 2018, Dessureault was a professor at the University of Arizona, where he engaged in research related to the integration and effective use of mining information systems and sustainability. In 2004, he founded MISOM Technologies, an IT services company with expertise in data integration, control rooms, and automation. MISOM eventually developed the FARA platform, which brings together Internet of Things (IoT) sensors, a tablet-based mobile app, and modern approaches such as Big Data and gamification to create innovative form digitization, worker engagement, and fleet management. In late 2017, MISOM was acquired by MST Global, where Dr. Dessureault served as Chief Innovation Evangelist until joining Mosaic in 2019.

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“…. ., x n ), the RBF approximation is given by the function presented in the following equation [36].…”

Section: Radial Basis Function Neuralmentioning

confidence: 99%

A Risk-Free Discount Rate Prediction Model for Mineral Project Evaluation Using a Hybrid Discrete Wavelet Transform and Artificial Neural Network

Gyebuni

Ziggah

Mireku-Gyimah

2022

Mathematical Problems in Engineering

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The discount rate input parameter of Net Present Value (NPV) in mineral project evaluation is a function of a risk-free rate and risk premium component. To obtain a reliable NPV, it is important to estimate each of these components. This study employs a hybrid approach to predict risk-free rate using Discrete Wavelet Transform and Artificial Neural Network (DWT-ANN). The DWT-ANN model was tested using London Interbank Offered Rate (LIBOR) dataset from 1986 to 2020. The results showed that Discrete Wavelet Transform-Radial Basis Function Neural Network (DWT-RBFNN) of the three different hybrid algorithms developed and applied performed best in predicting the risk-free rate. This is because it achieved the lowest root mean square error of 0.0376 and the highest correlation coefficient of 0.9995. The DWT-RBFNN model can be a useful alternative tool for predicting risk-free rate, which is a key input parameter for the determination of discount rate.

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Soft computing-based technique as a predictive tool to estimate blast-induced ground vibration

Cited by 3 publications

References 0 publications

The effect of EGR and hydrogen addition to natural gas on performance and exhaust emissions in a diesel engine by AVL fire multi-domain simulation, GPR model, and multi-objective genetic algorithm

The effect of EGR and hydrogen addition to natural gas on performance and exhaust emissions in a diesel engine by AVL fire multi-domain simulation, GPR model, and multi-objective genetic algorithm

Advances in Computational Intelligence Applications in the Mining Industry

A Risk-Free Discount Rate Prediction Model for Mineral Project Evaluation Using a Hybrid Discrete Wavelet Transform and Artificial Neural Network

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