Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

Zhou, Jian; Qiu, Yingui; Zhu, Shuangli; Armaghani, Danial Jahed; Li, Chuanqi; Nguyen, Hoang; Yağız, Saffet

doi:10.1016/j.engappai.2020.104015

Cited by 222 publications

(59 citation statements)

References 110 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ese algorithms are used to optimize the performance of machine learning model to achieve a balance between model accuracy and model generalization. e employed metaheuristic approaches include symbiotic organisms search [42], particle swarm optimization [43,44], the forensic-based investigation optimization [45], equilibrium optimization [20], Harris hawks optimization [46], simulated annealing [47], social spider optimization [48,49], gray wolf optimization [38,50], teaching-learningbased algorithm [51], salp swarm algorithm [52,53], artificial bee colony [54], pigeon-inspired optimization [55], cuckoo search optimization [56], imperialist competitive algorithm [57], moth flame optimization [58], and cuckoo search algorithm [59]. ose previous works have demonstrated the effectiveness of metaheuristic algorithms in optimizing machine learning models and solving complex tasks in various application domains.…”

Section: Research Background and Motivationmentioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

show abstract

Section: Research Background and Motivationmentioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…For the comparison of the model performance, three performance metrics including R 2 , RMSE, and VAF were applied, and a prediction model can be considered as the best model when R 2 � 1, RMSE � 0, and VAF � 100. Meanwhile, the value of these performance metrics can be calculated using the following formula [32,[82][83][84][85][86][87]: where N, y, y, and y pre are the number of datasets, the average PPV values, the actual PPV values, and the predicted PPV values, respectively.…”

Section: Performance Of Various Modelsmentioning

confidence: 99%

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Jiang¹,

Xu²,

Chen

et al. 2021

Advances in Civil Engineering

Self Cite

View full text Add to dashboard Cite

The accurate determination of blast-induced ground vibration has an important significance in protecting human activities and the surrounding environment. For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. In this regard, 88 datasets with 9 variables including the ratio of bench height to burden (H/B) and the ratio of spacing to burden (S/B) were selected as the input variables, while peak particle velocity (PPV) was measured. Then, an ANN model, an SVR model, a GP model, an SSA-GP model, and three empirical models were established, and the predictive performance was evaluated by using the root-mean-square error (RMSE), determination coefficient (R2), value account for (VAF), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and the run time. After comparing, it is found that the proposed SSA-GP yielded a superior performance and the ratio of bench height to burden (H/B) was the most sensitive variable.

show abstract

“…The commonly used optimization algorithms include the GA, the simulated annealing (SA) algorithm, the PSO algorithm, and the Bayesian optimization (BO) algorithm. Among these algorithms [26][27][28][29][30][31][32], the BO algorithm whose iterations are few is able to quickly find the optimal value without wasting resources. Such a feature enables this algorithm to be applicable to the coal mine field.…”

Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory Networks and Bayesian Optimization for Predicting the Time-Weighted Average Pressure of Shield Supporting Cycles

et al. 2021

View full text Add to dashboard Cite

Characteristic parameters of shield supporting in fully mechanized mining, especially time-weighted average pressure (TWAP), are crucial for the analysis and prediction of roof weightings in longwall panels. Despite the leap-forward development of underground data collection and transmission, mining and regional correlation analysis of massive shield data remains challenging. In this study, a hybrid machine learning model integrating the long short-term memory (LSTM) networks and the Bayesian optimization (BO) algorithm was developed to predict TWAP based on the setting pressure (SP), revised setting pressure (RSP), final pressure (FP), number of yielding (NY), TWAP in the last supporting cycle (TWAP (last)), and loading rate in each period. Statistical measures including the mean square error and mean absolute error were used to validate and compare the prediction performances of the BP model, the LSTM model, and the BO-LSTM model. Furthermore, sensitivity studies were carried out to evaluate the importance of input parameters. The results show that the BO-LSTM model is robust in predicting TWAP. FP and TWAP (last) are the most important input parameters in TWAP prediction, followed by RSP and NY. Moreover, the total importance scores of loading rates reach 0.229, indicating the necessity of including these parameters into the dataset. The proposed BO-LSTM model is capable of predicting TWAP which serves for shield-roof status intelligent perception.

show abstract

Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

Cited by 222 publications

References 110 publications

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Long Short-Term Memory Networks and Bayesian Optimization for Predicting the Time-Weighted Average Pressure of Shield Supporting Cycles

Contact Info

Product

Resources

About