TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm

Harandizadeh, Hooman; Armaghani, Danial Jahed; Gandomi, Amir H.

doi:10.1007/s00521-021-06217-x

Cited by 34 publications

(13 citation statements)

References 87 publications

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“…Generally, R 2 and VAF values equal to 100, and RMSE, MAE, and MAPE values equal to 0 indicate the best predictive performance of a model. The interpretation of these indicators is given in the literature (75-89), and their expressions are shown in Equations (22)(23)(24)(25)(26).…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Zhou

Chen

et al. 2023

Front. Public Health

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Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253).

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Section: Evaluation Indicatorsmentioning

confidence: 99%

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Zhou

Chen

et al. 2023

Front. Public Health

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“…Moreover, the technique also anticipates nonlinear statistical data. Using an ANFIS model, Harandizadeh et al [ 35 ] created a novel hybrid intelligence system, ANFIS-PNN-ICA, that combined an adaptive neuro-fuzzy inference system (ANFIS) with a polynomial neural network (PNN), improved using the ICA algorithm i.e. Imperialism competitive algorithm for forecasting TBM performance.…”

Section: Preliminariesmentioning

confidence: 99%

Assessing cloud QoS predictions using OWA in neural network methods

Hussain

Gao

Raza

et al. 2022

Neural Comput & Applic

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Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.

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“…In these equations, BB i is the real value, B B i stands for the forecasted value, BB i implies the mean of the real values, and N signifies the number of samples in the training or testing phases. It is important to mention that these performance criteria have been used in many published works (e.g., [60][61][62][63][64][65]).…”

Section: Models' Development and Evaluationmentioning

confidence: 99%

Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation

Monjezi

Mohammed

et al. 2021

Sustainability

Self Cite

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Back-break is an adverse event in blasting works that causes the instability of mine walls, equipment collapsing, and reduction in effectiveness of drilling. Therefore, it boosts the total cost of mining operations. This investigation intends to develop optimized support vector machine models to forecast back-break caused by blasting. The Support Vector Machine (SVM) model was optimized using two advanced metaheuristic algorithms, including whale optimization algorithm (WOA) and moth–flame optimization (MFO). Before the models’ development, an evolutionary random forest (ERF) technique was used for input selection. This model selected five inputs out of 10 candidate inputs to be used to predict the back break. These two optimized SVM models were evaluated using various performance criteria. The performance of these two models was also compared with other hybridized SVM models. In addition, a sensitivity evaluation was made to find how the selected inputs influence the back-break magnitude. The outcomes of this study demonstrated that both the SVM–MFO and SVM–WOA improved the performance of the standard SVM. Additionally, the SVM–MFO showed a better performance than the SVM–WOA and other hybridized SVM models. The outcomes of this research recommend that the SVM–MFO can be considered as a powerful model to forecast the back-break induced by blasting.

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TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm

Cited by 34 publications

References 87 publications

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Assessing cloud QoS predictions using OWA in neural network methods

Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation

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