Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

Danial Jahed Armaghani, Panagiotis G. Asteris, Behnam Askarian, Mahdi Hasanipanah, Reza Tarinejad and Huynh Van Van
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Danial Jahed Armaghani: Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
Panagiotis G. Asteris: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Heraklion, Athens, Greece
Behnam Askarian: Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA
Mahdi Hasanipanah: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Reza Tarinejad: Department of Civil Engineering, University of Tabriz, 29 Bahman Blvd, Tabriz 51666, Iran
Huynh Van Van: Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam

Sustainability, 2020, vol. 12, issue 6, 1-17

Abstract: The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models. To this end, the authors developed eight SVM models with different kernel types, i.e., the radial basis function (RBF), the linear (LIN), the sigmoid (SIG), and the polynomial (POL). Four of these models were developed using only the SVM method, while the four other models were hybridized with a feature selection (FS) technique. The performance of each model was assessed using five performance indices and a simple ranking system. The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models. Concerning the importance of variables for predicting the brittleness index (BI), the Schmidt hammer rebound number (R n ) was identified as the most important variable by the three single-based models, developed by POL, SIG, and LIN kernels. However, the single SVM model developed by RBF identified density as the most important input variable. Concerning the hybrid SVM models, three models that were developed using the RBF, POL, and SIG kernels identified the point load strength index as the most important input, while the model developed using the LIN identified the R n as the most important input. All four single-based SVM models identified the p-wave velocity (V p ) as the least important input. Concerning the least important factors for predicting the BI of the rock in hybrid-based models, V p was identified as the least important factor by FS-SVM-POL, FS-SVM-SIG, and FS-SVM-LIN, while the FS-SVM-RBF identified R n as the least important input.

Keywords: support vector machine; kernel; feature selection; rock brittleness; input importance (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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