Predicting Calorific Value of Thar Lignite Deposit: A Comparison between Back-propagation Neural Networks (BPNN), Gradient Boosting Trees (GBT), and Multiple Linear Regression (MLR)

Ahmed, Waqas; Muhammad, Khan; Siddiqui, Fahad Irfan

doi:10.1080/08839514.2020.1824091

Cited by 9 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A BP neural network (BPNN) is a multilayer forward network based on error backpropagation, consisting primarily of an input layer, several hidden layers, and an output layer. It has strong nonlinear fitting capabilities and has some practicality in classifying, identifying, and calculating risk values [ 4 , 5 ]. BPNN can classify any complex pattern and has excellent multidimensional function mapping capabilities.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Safety Risk Assessment of Tourism Management System Based on PSO‐BP Neural Network

Guo

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the development of science and technology, system management is gradually applied to tourism management. How to correctly assess the security risks of the tourism management system has become an important means to maintain passenger information. The security risk index of the travel management system is input into the PSO-BP network as a sample, and the corresponding risk value of the index is used as the network output. The results show that the error results, accuracy (96.53%), training time (216 s), number of iterations (275 times), and convergence speed are all better than traditional BP network. The relative error of PSO-BP network (0.32%) is better than that of BP network, with 300 iterations, and the error is close to 10–5. The average evaluation accuracy of S based on PSO-BP network is 99.72%, and the average time consumed is 2.512 s. It is superior to the evaluation model based on fuzzy set and entropy weight theory and the evaluation model based on gray correlation analysis and radial basis function neural network. In conclusion, the security risk assessment of the tourism management system based on PSO-BP network can effectively assess the security risk of the tourism management system.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Safety Risk Assessment of Tourism Management System Based on PSO‐BP Neural Network

Guo

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The Thar Coalfield in Pakistan ranks as the seventh-largest coal field globally (Ahmed et al, 2020). The Thar Coalfield consists…”

Section: Methodsmentioning

confidence: 99%

“…FIGURE 2Geographical map of Block IX of the Thar Coalfield region [modified after(Ahmed et al, 2020)]. …”

mentioning

confidence: 99%

Hybrid PSO with tree-based models for predicting uniaxial compressive strength and elastic modulus of rock samples

Shahani,

Xiaowei,

Wei

et al. 2024

Front. Earth Sci.

View full text Add to dashboard Cite

The mechanical characteristics of rocks, specifically uniaxial compressive strength (UCS) and elastic modulus (E), serve as crucial factors in ensuring the integrity and stability of relevant projects in mining and civil engineering. This study proposes a novel hybrid PSO (particle swarm optimization) with tree-based models, such as gradient boosting regressor (GBR), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) for predicting UCS and E of rock samples from Block IX of the Thar Coalfield in Pakistan. A total of 122 datasets were divided into training and testing sets, with an 80:20 ratio, respectively, to develop the predictive models. Key performance metrics, including the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were employed to assess the model’s predictive performance. The results indicate that the PSO-XGBoost model demonstrated the highest accuracy in predicting UCS and E, outperforming the other models, which exhibited inferior predictive performance. Furthermore, this study utilized the SHAP (Shapley Additive exPlanations) machine learning method to enhance our understanding of how each input feature variable influences the output values of UCS and E. In conclusion, the proposed framework offers significant advantages in evaluating the strength and deformation of rocks at Thar Coalfield, with promising applications in the field of mining and rock engineering.

show abstract

“…The application of smart tree-based intelligent methods is limited in GCV prediction. Tree-based intelligent methods, namely regression tree, random forest, gradient boosting tree, and XGBoost were applied by Bui et al [ 8 ], Matin and Chelgani [ 26 ], Ahmed et al [ 27 ], and Chelgani [ 28 ], respectively ( Table 2 ). They applied these techniques to different datasets with different sample sizes.…”

Section: Introductionmentioning

confidence: 99%

“…TOB is optimized using memetic firefly optimizer, generalized reduced gradient, and evolutionary algorithm. 2020 Ahmed et al [ 27 ] C M , V M , C A , FC CCV BPNN GBT MLR R 2 : 0.89 R 2 : 0.91 R 2 : 0.80 R 2 8039 Yes 1. GBT performed better than BPNN, and MLR.…”

Section: Introductionmentioning

confidence: 99%

Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques

Munshi,

Jahan,

Howladar

et al. 2024

Heliyon

View full text Add to dashboard Cite

Predicting Calorific Value of Thar Lignite Deposit: A Comparison between Back-propagation Neural Networks (BPNN), Gradient Boosting Trees (GBT), and Multiple Linear Regression (MLR)

Cited by 9 publications

References 23 publications

[Retracted] Safety Risk Assessment of Tourism Management System Based on PSO‐BP Neural Network

[Retracted] Safety Risk Assessment of Tourism Management System Based on PSO‐BP Neural Network

Hybrid PSO with tree-based models for predicting uniaxial compressive strength and elastic modulus of rock samples

Prediction of gross calorific value from coal analysis using decision tree-based bagging and boosting techniques

Contact Info

Product

Resources

About