Predicting the stability of hard rock pillars using multinomial logistic regression

Wattimena, Ridho K.

doi:10.1016/j.ijrmms.2014.03.015

Cited by 26 publications

(11 citation statements)

References 3 publications

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“…With the accumulation of pillar stability cases, it provides researchers the opportunity to develop advanced predictive models using ML algorithms. Tawadrous and Katsabanis [22] used the artificial neural networks (ANN) to analyze the stability of surface crown pillars; Wattimena [23] introduced the multinomial logistic regression (MLR) for pillar stability prediction; Ding et al [24] adopted a stochastic gradient boosting (SGB) model to predict pillar stability, and found that this model achieved a better performance than the random forest (RF), support vector machine (SVM), and multilayer perceptron neural networks (MPNN); Ghasemi et al [25] utilized J48 algorithm and SVM to develop two pillar stability graphs, and obtained acceptable prediction accuracy; and Zhou et al [9] compared the prediction performance of pillar stability using six supervised learning methods, and revealed that SVM and RF performed better. Although these ML algorithms can solve pillar stability prediction issues to some extent, none can be applied to all engineering conditions.…”

Section: Introductionmentioning

confidence: 99%

Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

et al. 2020

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Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great potential to predict pillar stability. This study aims to predict hard rock pillar stability using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms. First, 236 cases with five indicators were collected from seven hard rock mines. Afterwards, the hyperparameters of each model were tuned using a five-fold cross validation (CV) approach. Based on the optimal hyperparameters configuration, prediction models were constructed using training set (70% of the data). Finally, the test set (30% of the data) was adopted to evaluate the performance of each model. The precision, recall, and F1 indexes were utilized to analyze prediction results of each level, and the accuracy and their macro average values were used to assess the overall prediction performance. Based on the sensitivity analysis of indicators, the relative importance of each indicator was obtained. In addition, the safety factor approach and other ML algorithms were adopted as comparisons. The results showed that GBDT, XGBoost, and LightGBM algorithms achieved a better comprehensive performance, and their prediction accuracies were 0.8310, 0.8310, and 0.8169, respectively. The average pillar stress and ratio of pillar width to pillar height had the most important influences on prediction results. The proposed methodology can provide a reliable reference for pillar design and stability risk management.

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Section: Introductionmentioning

confidence: 99%

Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

et al. 2020

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“…Data rockburst tersebut selanjutnya dievaluasi dengan metode regresi logistik yang lebih sederhana dibandingkan dengan kelima metode di atas. Regresi ini telah digunakan untuk memprediksi caveability massa batuan (Mawdesley et al, 2001) maupun kemantapan pilar pada tambang bawah tanah (Wattimena et al, 2013;Wattimena, 2014). Regresi ini dipilih mengingat variable tak-bebas (tingkat rockburst) pada dataset hanya mempunyai empat nilai yang mungkin yaitu 0 (tidak ada rockburst), 1 (rockburst ringan), 2 (rockburst sedang), dan 3 (rockburst berat).…”

Section: Pendahuluanunclassified

Metode Regresi Logistik Untuk Memprediksi Risiko Rockburst Jangka Pendek

Wattimena

2021

ind. mining, prof. jurnal

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Rockburst telah menjadi salah satu risiko mayor pada konstruksi dan tambang bawah tanah batuan keras yang dalam. Banyak pendekatan telah diusulkan untk memprediski risiko rockburst, mulai dari pengujian laboratorium sederhana sampai kepada metode learning machine yang canggih. Makalah ini membandingkan hasil prediksi metode ensemble learning dan regresi logistik dan menunjukkan bahwa metode regresi logistik yang lebih sederhana dapat memberikan hasil yang lebih akurat.

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“…to calculate and interpret the effect of an independent variable; it is good to take exponential of both sides of the equation to get predicted probabilities (Wattimena 2014).…”

Section: B Multinomial Logistic Regressionmentioning

confidence: 99%

Machine learning model development for predicting road transport GHG emissions in Canada

Khan

Awasthi

2019

WSB Journal of Business and Finance

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Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.

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Predicting the stability of hard rock pillars using multinomial logistic regression

Cited by 26 publications

References 3 publications

Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

Metode Regresi Logistik Untuk Memprediksi Risiko Rockburst Jangka Pendek

Machine learning model development for predicting road transport GHG emissions in Canada

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