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Published in: Neural Computing and Applications 8/2021

28-07-2020 | Original Article

Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model

Authors: Qiancheng Fang, Hoang Nguyen, Xuan-Nam Bui, Trung Nguyen-Thoi, Jian Zhou

Published in: Neural Computing and Applications | Issue 8/2021

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Abstract

This paper proposes a new soft computing model (artificial intelligence model) for modeling rock fragmentation (i.e., the size distribution of rock (SDR)) with high accuracy, based on a boosted generalized additive model (BGAM) and a firefly algorithm (FFA), called FFA-BGAM. Accordingly, the FFA was used as a robust optimization algorithm/meta-heuristic algorithm to optimize the BGAM model. A split-desktop environment was used to analyze and calculate the size of rock from 136 images, which were captured from 136 blasts. To this end, blast designs were collected and extracted as the input parameters. Subsequently, the proposed FFA-BGAM model was evaluated and compared through previous well-developed soft computing models, such as FFA-ANN (artificial neural network), FFA-ANFIS (adaptive neuro-fuzzy inference system), support vector machine (SVM), Gaussian process regression (GPR), and k-nearest neighbors (KNN) based on three performance indicators (MAE, RMSE, and R2). The results indicated that the new intelligent technique (i.e., FFA-BGAM) provided the highest accuracy in predicting the SDR with an MAE of 0.920, RMSE of 1.213, and R2 of 0.980. In contrast, the remaining models (i.e., FFA-ANN, FFA-ANFIS, SVM, GPR, and KNN) yielded lower accuracies in predicting the SDR, i.e., MAEs of 1.248, 1.661, 1.096, 1.573, 1.237; RMSEs of 1.598, 2.068, 1.402, 2.137, 1.717; and R2 of 0.967, 0.968, 0.972, 0.940, 0.963, respectively.
Literature
8.
go back to reference Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani S (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7(12):5383–5396 Armaghani DJ, Hajihassani M, Mohamad ET, Marto A, Noorani S (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7(12):5383–5396
9.
go back to reference Li W-X, Qi D-L, Zheng S-F, Ren J-C, Li J-f, Yin X (2015) Fuzzy mathematics model and its numerical method of stability analysis on rock slope of opencast metal mine. Appl Math Model 39(7):1784–1793 MathSciNetMATH Li W-X, Qi D-L, Zheng S-F, Ren J-C, Li J-f, Yin X (2015) Fuzzy mathematics model and its numerical method of stability analysis on rock slope of opencast metal mine. Appl Math Model 39(7):1784–1793 MathSciNetMATH
10.
go back to reference Binh DV, Ha TTK, Hai DT, Cuong DC, Anh DL, Nam H (2020) Calculation of the exploited flow water in the T2ađg sediments at the well Kien Khe. Hanam. J Min Earth Sci 61(2):41–49 Binh DV, Ha TTK, Hai DT, Cuong DC, Anh DL, Nam H (2020) Calculation of the exploited flow water in the T2ađg sediments at the well Kien Khe. Hanam. J Min Earth Sci 61(2):41–49
11.
go back to reference Nguyen NV, Trinh HL (2020) Determination of water quality parameters in the Tan Rai exploiting area (Lam Dong province) using Sentinel-2 msi and Landsat 8 data. J Min Earth Sci 61(2):126–134 Nguyen NV, Trinh HL (2020) Determination of water quality parameters in the Tan Rai exploiting area (Lam Dong province) using Sentinel-2 msi and Landsat 8 data. J Min Earth Sci 61(2):126–134
12.
go back to reference Armaghani DJ, Hajihassani M, Marto A, Faradonbeh RS, Mohamad ET (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess 187(11):666 Armaghani DJ, Hajihassani M, Marto A, Faradonbeh RS, Mohamad ET (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess 187(11):666
15.
go back to reference Mahdevari S, Torabi SR, Monjezi M (2012) Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon. Int J Rock Mech Min Sci 55:33–44 Mahdevari S, Torabi SR, Monjezi M (2012) Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon. Int J Rock Mech Min Sci 55:33–44
16.
go back to reference De Simone M, Souza LM, Roehl D (2019) Estimating DEM microparameters for uniaxial compression simulation with genetic programming. Int J Rock Mech Min Sci 118:33–41 De Simone M, Souza LM, Roehl D (2019) Estimating DEM microparameters for uniaxial compression simulation with genetic programming. Int J Rock Mech Min Sci 118:33–41
18.
go back to reference Khandelwal M, Singh T (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46(7):1214–1222 Khandelwal M, Singh T (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46(7):1214–1222
19.
go back to reference Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48(5):845 Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48(5):845
20.
go back to reference Yurdakul M, Gopalakrishnan K, Akdas H (2014) Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology. Int J Rock Mech Min Sci 67:127–135 Yurdakul M, Gopalakrishnan K, Akdas H (2014) Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology. Int J Rock Mech Min Sci 67:127–135
21.
go back to reference Liu J, Zhao X-D, Xu Z-h (2017) Identification of rock discontinuity sets based on a modified affinity propagation algorithm. Int J Rock Mech Min Sci 94:32–42 Liu J, Zhao X-D, Xu Z-h (2017) Identification of rock discontinuity sets based on a modified affinity propagation algorithm. Int J Rock Mech Min Sci 94:32–42
23.
go back to reference Daftaribesheli A, Ataei M, Sereshki F (2011) Assessment of rock slope stability using the Fuzzy Slope Mass Rating (FSMR) system. Appl Soft Comput 11(8):4465–4473 Daftaribesheli A, Ataei M, Sereshki F (2011) Assessment of rock slope stability using the Fuzzy Slope Mass Rating (FSMR) system. Appl Soft Comput 11(8):4465–4473
24.
go back to reference Asadi M, Eftekhari M, Bagheripour MH (2011) Evaluating the strength of intact rocks through genetic programming. Appl Soft Comput 11(2):1932–1937 Asadi M, Eftekhari M, Bagheripour MH (2011) Evaluating the strength of intact rocks through genetic programming. Appl Soft Comput 11(2):1932–1937
25.
go back to reference Kang F, Xu B, Li J, Zhao S (2017) Slope stability evaluation using Gaussian processes with various covariance functions. Appl Soft Comput 60:387–396 Kang F, Xu B, Li J, Zhao S (2017) Slope stability evaluation using Gaussian processes with various covariance functions. Appl Soft Comput 60:387–396
26.
go back to reference Zhang H, Nguyen H, Bui X-N, Nguyen-Thoi T, Bui T-T, Nguyen N, Vu D-A, Mahesh V, Moayedi H (2020) Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm. Resour Policy 66:101604 Zhang H, Nguyen H, Bui X-N, Nguyen-Thoi T, Bui T-T, Nguyen N, Vu D-A, Mahesh V, Moayedi H (2020) Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm. Resour Policy 66:101604
27.
go back to reference Nguyen HV, Bui ST, Phung HH, Pham HNT (2020) The initial research on the compressive strength of mortar when using bottom ash from thermal power plants to replace natural sand in construction. J Min Earth Sci 61(3):12–18 Nguyen HV, Bui ST, Phung HH, Pham HNT (2020) The initial research on the compressive strength of mortar when using bottom ash from thermal power plants to replace natural sand in construction. J Min Earth Sci 61(3):12–18
28.
go back to reference Tran TM, Do TN, Dinh HTT, Vu HX, Ferrier E (2020) A 2-D numerical model of the mechanical behavior of the textile-reinforced concrete composite material: effect of textile reinforcement ratio. J Min Earth Sci 61(3):51–59 Tran TM, Do TN, Dinh HTT, Vu HX, Ferrier E (2020) A 2-D numerical model of the mechanical behavior of the textile-reinforced concrete composite material: effect of textile reinforcement ratio. J Min Earth Sci 61(3):51–59
29.
go back to reference Nguyen LQ (2020) A novel approach of determining the parameters of Asadi profiling function for predicting ground subsidence due to inclined coal seam mining at Quang Ninh coal basin. J Min Earth Sci 61(2):86–95 Nguyen LQ (2020) A novel approach of determining the parameters of Asadi profiling function for predicting ground subsidence due to inclined coal seam mining at Quang Ninh coal basin. J Min Earth Sci 61(2):86–95
30.
go back to reference Nguyen NTT, Tong HT (2020) Predicting land use change base on GIS and remote sensing. J Min Earth Sci 61(2):106–115 MathSciNet Nguyen NTT, Tong HT (2020) Predicting land use change base on GIS and remote sensing. J Min Earth Sci 61(2):106–115 MathSciNet
31.
go back to reference Pham LT, Nguyen SP, Nguyen NV, Dao HV, Doan LD, Vo NHT, Nguyen TTT, Tran HV (2020) Establishment of land cover map using object-oriented classification method for VNREDSat-1 data. J Min Earth Sci 61(2):134–144 Pham LT, Nguyen SP, Nguyen NV, Dao HV, Doan LD, Vo NHT, Nguyen TTT, Tran HV (2020) Establishment of land cover map using object-oriented classification method for VNREDSat-1 data. J Min Earth Sci 61(2):134–144
32.
go back to reference Nghia NV (2020) Building DEM for deep open-pit coal mines using DJI Inspire 2. J Min Earth Sci 61(1):1–10 Nghia NV (2020) Building DEM for deep open-pit coal mines using DJI Inspire 2. J Min Earth Sci 61(1):1–10
33.
go back to reference Canh LV, Cuong CX, Tien D (2020) Volume computation of quarries in Vietnam based on Unmanned Aerial Vehicle (UAV) data. J Min Earth Sci 61(1):21–30 Canh LV, Cuong CX, Tien D (2020) Volume computation of quarries in Vietnam based on Unmanned Aerial Vehicle (UAV) data. J Min Earth Sci 61(1):21–30
34.
go back to reference Sinh VT, Quyen VT, Huan LN, Huong TT (2020) Design information orientation supporting system for user. J Min Earth Sci 61(1):41–51 Sinh VT, Quyen VT, Huan LN, Huong TT (2020) Design information orientation supporting system for user. J Min Earth Sci 61(1):41–51
35.
go back to reference Nam DV, Oanh NT, Hoai NX, Manh NV, Hien NT (2020) Detect and process outliers for temperature data at 3h monitoring stations in Vietnam. J Min Earth Sci 61(1):132–146 Nam DV, Oanh NT, Hoai NX, Manh NV, Hien NT (2020) Detect and process outliers for temperature data at 3h monitoring stations in Vietnam. J Min Earth Sci 61(1):132–146
36.
go back to reference Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455 Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455
37.
go back to reference Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MM (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043–1050 Hasanipanah M, Armaghani DJ, Amnieh HB, Majid MZA, Tahir MM (2017) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl 28(1):1043–1050
38.
go back to reference Esmaeili M, Salimi A, Drebenstedt C, Abbaszadeh M, Bazzazi AA (2015) Application of PCA, SVR, and ANFIS for modeling of rock fragmentation. Arab J Geosci 8(9):6881–6893 Esmaeili M, Salimi A, Drebenstedt C, Abbaszadeh M, Bazzazi AA (2015) Application of PCA, SVR, and ANFIS for modeling of rock fragmentation. Arab J Geosci 8(9):6881–6893
39.
go back to reference Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75(1):27–36 Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75(1):27–36
40.
go back to reference Hasanipanah M, Amnieh HB, Arab H, Zamzam MS (2018) Feasibility of PSO-ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015–1024 Hasanipanah M, Amnieh HB, Arab H, Zamzam MS (2018) Feasibility of PSO-ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput Appl 30(4):1015–1024
42.
go back to reference Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2018) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34(2):241–251 Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2018) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34(2):241–251
43.
go back to reference Mojtahedi SFF, Ebtehaj I, Hasanipanah M, Bonakdari H, Amnieh HB (2019) Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng Comput 35(1):47–56 Mojtahedi SFF, Ebtehaj I, Hasanipanah M, Bonakdari H, Amnieh HB (2019) Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng Comput 35(1):47–56
44.
go back to reference Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8(12):10819–10832 Shams S, Monjezi M, Majd VJ, Armaghani DJ (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8(12):10819–10832
45.
go back to reference Hasanipanah M, Armaghani DJ, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75(9):808 Hasanipanah M, Armaghani DJ, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75(9):808
46.
go back to reference Monjezi M, Mohamadi HA, Barati B, Khandelwal M (2014) Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects. Arab J Geosci 7(2):505–511 Monjezi M, Mohamadi HA, Barati B, Khandelwal M (2014) Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects. Arab J Geosci 7(2):505–511
47.
go back to reference Amodio S (2011) Generalized boosted additive models. University of Naples Federico II, Napoli Amodio S (2011) Generalized boosted additive models. University of Naples Federico II, Napoli
48.
go back to reference Le LT, Nguyen H, Zhou J, Dou J, Moayedi H (2019) Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost. Appl Sci 9(13):2714 Le LT, Nguyen H, Zhou J, Dou J, Moayedi H (2019) Estimating the heating load of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost. Appl Sci 9(13):2714
49.
go back to reference Le LT, Nguyen H, Dou J, Zhou J (2019) A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Appl Sci 9(13):2630 Le LT, Nguyen H, Dou J, Zhou J (2019) A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Appl Sci 9(13):2630
56.
go back to reference Xu Y-T, Zhang Y, Wang S-G (2015) A modified tunneling function method for non-smooth global optimization and its application in artificial neural network. Appl Math Model 39(21):6438–6450 MathSciNetMATH Xu Y-T, Zhang Y, Wang S-G (2015) A modified tunneling function method for non-smooth global optimization and its application in artificial neural network. Appl Math Model 39(21):6438–6450 MathSciNetMATH
57.
go back to reference Aich U, Banerjee S (2014) Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization. Appl Math Model 38(11–12):2800–2818 MATH Aich U, Banerjee S (2014) Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization. Appl Math Model 38(11–12):2800–2818 MATH
59.
go back to reference Nguyen H, Choi Y, Bui X-N, Nguyen-Thoi T (2020) Predicting blast-induced ground vibration in open-pit mines using vibration sensors and support vector regression-based optimization algorithms. Sensors 20(1):132 Nguyen H, Choi Y, Bui X-N, Nguyen-Thoi T (2020) Predicting blast-induced ground vibration in open-pit mines using vibration sensors and support vector regression-based optimization algorithms. Sensors 20(1):132
61.
go back to reference Zhang X, Nguyen H, Bui X-N, Le HA, Nguyen-Thoi T, Moayedi H, Mahesh V (2020) Evaluating and predicting the stability of roadways in tunnelling and underground space using artificial neural network-based particle swarm optimization. Tunn Undergr Space Technol 103:103517 Zhang X, Nguyen H, Bui X-N, Le HA, Nguyen-Thoi T, Moayedi H, Mahesh V (2020) Evaluating and predicting the stability of roadways in tunnelling and underground space using artificial neural network-based particle swarm optimization. Tunn Undergr Space Technol 103:103517
62.
63.
go back to reference Hastie TJ (2017) Generalized additive models. In: Hastie TJ, Tibshirani RJ (eds) Statistical models in S. Routledge, Abingdon, pp 249–307 Hastie TJ (2017) Generalized additive models. In: Hastie TJ, Tibshirani RJ (eds) Statistical models in S. Routledge, Abingdon, pp 249–307
64.
go back to reference Hastie T, Tibshirani R (1990) Generalized additive models. Chapman and Hall Inc, London MATH Hastie T, Tibshirani R (1990) Generalized additive models. Chapman and Hall Inc, London MATH
65.
go back to reference Marx BD, Eilers PH (1998) Direct generalized additive modeling with penalized likelihood. Comput Stat Data Anal 28(2):193–209 MATH Marx BD, Eilers PH (1998) Direct generalized additive modeling with penalized likelihood. Comput Stat Data Anal 28(2):193–209 MATH
66.
go back to reference Wand MP (2000) A comparison of regression spline smoothing procedures. Comput Stat 15(4):443–462 MathSciNetMATH Wand MP (2000) A comparison of regression spline smoothing procedures. Comput Stat 15(4):443–462 MathSciNetMATH
67.
go back to reference Fahrmeir L, Lang S (2001) Bayesian inference for generalized additive mixed models based on Markov random field priors. J R Stat Soc: Ser C (Appl Stat) 50(2):201–220 MathSciNet Fahrmeir L, Lang S (2001) Bayesian inference for generalized additive mixed models based on Markov random field priors. J R Stat Soc: Ser C (Appl Stat) 50(2):201–220 MathSciNet
68.
go back to reference Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28(2):337–407 MATH Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28(2):337–407 MATH
69.
go back to reference Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer series in statistics, vol 10. Springer, New York, NY MATH Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer series in statistics, vol 10. Springer, New York, NY MATH
70.
go back to reference Tutz G, Binder H (2006) Generalized additive modeling with implicit variable selection by likelihood-based boosting. Biometrics 62(4):961–971 MathSciNetMATH Tutz G, Binder H (2006) Generalized additive modeling with implicit variable selection by likelihood-based boosting. Biometrics 62(4):961–971 MathSciNetMATH
71.
go back to reference Leathwick J, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199(2):188–196 Leathwick J, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199(2):188–196
72.
go back to reference Mayr A, Fenske N, Hofner B, Kneib T, Schmid M (2012) Generalized additive models for location, scale and shape for high dimensional data—a flexible approach based on boosting. J R Stat Soc: Ser C (Appl Stat) 61(3):403–427 MathSciNet Mayr A, Fenske N, Hofner B, Kneib T, Schmid M (2012) Generalized additive models for location, scale and shape for high dimensional data—a flexible approach based on boosting. J R Stat Soc: Ser C (Appl Stat) 61(3):403–427 MathSciNet
73.
go back to reference Horowitz JL (2001) Nonparametric estimation of a generalized additive model with an unknown link function. Econometrica 69(2):499–513 MathSciNetMATH Horowitz JL (2001) Nonparametric estimation of a generalized additive model with an unknown link function. Econometrica 69(2):499–513 MathSciNetMATH
74.
go back to reference de Brogniez D, Ballabio C, Stevens A, Jones R, Montanarella L, van Wesemael B (2015) A map of the topsoil organic carbon content of Europe generated by a generalized additive model. Eur J Soil Sci 66(1):121–134 de Brogniez D, Ballabio C, Stevens A, Jones R, Montanarella L, van Wesemael B (2015) A map of the topsoil organic carbon content of Europe generated by a generalized additive model. Eur J Soil Sci 66(1):121–134
75.
go back to reference Walsh WA, Kleiber P (2001) Generalized additive model and regression tree analyses of blue shark ( Prionace glauca) catch rates by the Hawaii-based commercial longline fishery. Fish Res 53(2):115–131 Walsh WA, Kleiber P (2001) Generalized additive model and regression tree analyses of blue shark ( Prionace glauca) catch rates by the Hawaii-based commercial longline fishery. Fish Res 53(2):115–131
76.
go back to reference Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017) Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69–85 Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S (2017) Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology 297:69–85
77.
go back to reference Liu X, Song Y, Yi W, Wang X, Zhu J (2018) Comparing the random forest with the generalized additive model to evaluate the impacts of outdoor ambient environmental factors on scaffolding construction productivity. J Constr Eng Manag 144(6):04018037 Liu X, Song Y, Yi W, Wang X, Zhu J (2018) Comparing the random forest with the generalized additive model to evaluate the impacts of outdoor ambient environmental factors on scaffolding construction productivity. J Constr Eng Manag 144(6):04018037
78.
go back to reference Marra G, Wood SN (2011) Practical variable selection for generalized additive models. Comput Stat Data Anal 55(7):2372–2387 MathSciNetMATH Marra G, Wood SN (2011) Practical variable selection for generalized additive models. Comput Stat Data Anal 55(7):2372–2387 MathSciNetMATH
79.
go back to reference Binder H, Tutz G (2008) A comparison of methods for the fitting of generalized additive models. Stat Comput 18(1):87–99 MathSciNet Binder H, Tutz G (2008) A comparison of methods for the fitting of generalized additive models. Stat Comput 18(1):87–99 MathSciNet
80.
go back to reference Groll A, Tutz G (2012) Regularization for generalized additive mixed models by likelihood-based boosting. Methods Inf Med 51(02):168–177 Groll A, Tutz G (2012) Regularization for generalized additive mixed models by likelihood-based boosting. Methods Inf Med 51(02):168–177
81.
go back to reference Fister I, Fister I Jr, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46 Fister I, Fister I Jr, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46
82.
go back to reference Zhou J, Nekouie A, Arslan CA, Pham BT, Hasanipanah M (2019) Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Eng Comput 36:1–10 Zhou J, Nekouie A, Arslan CA, Pham BT, Hasanipanah M (2019) Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Eng Comput 36:1–10
83.
go back to reference Ramezanian R, Saidi-Mehrabad M (2013) Hybrid simulated annealing and MIP-based heuristics for stochastic lot-sizing and scheduling problem in capacitated multi-stage production system. Appl Math Model 37(7):5134–5147 MathSciNetMATH Ramezanian R, Saidi-Mehrabad M (2013) Hybrid simulated annealing and MIP-based heuristics for stochastic lot-sizing and scheduling problem in capacitated multi-stage production system. Appl Math Model 37(7):5134–5147 MathSciNetMATH
86.
go back to reference Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2017) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34:1–11 Asl PF, Monjezi M, Hamidi JK, Armaghani DJ (2017) Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng Comput 34:1–11
87.
go back to reference Faradonbeh RS, Armaghani DJ, Amnieh HB, Mohamad ET (2018) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Appl 29(6):269–281 Faradonbeh RS, Armaghani DJ, Amnieh HB, Mohamad ET (2018) Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Comput Appl 29(6):269–281
88.
go back to reference Yang X-S (2017) Nature-inspired algorithms and applied optimization, vol 744. Springer, Berlin Yang X-S (2017) Nature-inspired algorithms and applied optimization, vol 744. Springer, Berlin
89.
go back to reference Aarts E, Aarts EH, Lenstra JK (2003) Local search in combinatorial optimization. Princeton University Press, Princeton MATH Aarts E, Aarts EH, Lenstra JK (2003) Local search in combinatorial optimization. Princeton University Press, Princeton MATH
90.
go back to reference Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181 Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27(2):177–181
91.
go back to reference Enayatollahi I, Bazzazi AA, Asadi A (2014) Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mech Rock Eng 47(2):799–807 Enayatollahi I, Bazzazi AA, Asadi A (2014) Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mech Rock Eng 47(2):799–807
92.
go back to reference Armaghani DJ (2018) Rock fragmentation prediction through a new hybrid model based on imperial competitive algorithm and neural network. Smart Constr Res 2:1–12 Armaghani DJ (2018) Rock fragmentation prediction through a new hybrid model based on imperial competitive algorithm and neural network. Smart Constr Res 2:1–12
93.
go back to reference Nazari A, Milani AA, Khalaj G (2012) Modeling ductile to brittle transition temperature of functionally graded steels by ANFIS. Appl Math Model 36(8):3903–3915 Nazari A, Milani AA, Khalaj G (2012) Modeling ductile to brittle transition temperature of functionally graded steels by ANFIS. Appl Math Model 36(8):3903–3915
94.
go back to reference Abdulshahed AM, Longstaff AP, Fletcher S, Myers A (2015) Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Appl Math Model 39(7):1837–1852 MATH Abdulshahed AM, Longstaff AP, Fletcher S, Myers A (2015) Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Appl Math Model 39(7):1837–1852 MATH
95.
go back to reference Asteris PG, Apostolopoulou M, Skentou AD, Moropoulou A (2019) Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars. Comput Concrete 24(4):329–345 Asteris PG, Apostolopoulou M, Skentou AD, Moropoulou A (2019) Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars. Comput Concrete 24(4):329–345
96.
go back to reference Sakia R (1992) The Box–Cox transformation technique: a review. The Statistician 41:169–178 Sakia R (1992) The Box–Cox transformation technique: a review. The Statistician 41:169–178
Metadata
Title
Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model
Authors
Qiancheng Fang
Hoang Nguyen
Xuan-Nam Bui
Trung Nguyen-Thoi
Jian Zhou
Publication date
28-07-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 8/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05197-8

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