ORCID Profile
0000-0002-1877-3859
Current Organisation
University of South Australia
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Publisher: MDPI AG
Date: 20-09-2022
DOI: 10.3390/SU141911843
Abstract: The occurrence of premature rockbolt failure in underground mines has remained one of the most serious challenges facing the industry over the years. Considering the complex mechanism of rockbolts’ failure and the large number of influencing factors, the prediction of rockbolts’ failure from laboratory testing may often be unreliable. It is therefore essential to develop new models capable of predicting rockbolts’ failure with high accuracy. Beyond the predictive accuracy, there is also the need to understand the decisions made by these models in order to convey trust and ensure safety, reliability, and accountability. In this regard, this study proposes an explainable risk assessment of rockbolts’ failure in an underground coal mine using the categorical gradient boosting (Catboost) algorithm and SHapley Additive exPlanations (SHAP). A dataset (including geotechnical and environmental features) from a complex underground mining environment was used. The outcomes of this study indicated that the proposed Catboost algorithm gave an excellent prediction of the risk of rockbolts’ failure. Additionally, the SHAP interpretation revealed that the “length of roadway” was the main contributing factor to rockbolts’ failure. However, conditions influencing rockbolts’ failure varied at different locations in the mine. Overall, this study provides insights into the complex relationship between rockbolts’ failure and the influence of geotechnical and environmental variables. The transparency and explainability of the proposed approach have the potential to facilitate the adoption of explainable machine learning for rockbolt risk assessment in underground mines.
Publisher: MDPI AG
Date: 02-11-2022
DOI: 10.3390/W14213509
Abstract: There is growing tension between high-performance machine-learning (ML) models and explainability within the scientific community. In arsenic modelling, understanding why ML models make certain predictions, for instance, “high arsenic” instead of “low arsenic”, is as important as the prediction accuracy. In response, this study aims to explain model predictions by assessing the relationship between influencing input variables, i.e., pH, turbidity (Turb), total dissolved solids (TDS), and electrical conductivity (Cond), on arsenic mobility. The two main objectives of this study are to: (i) classify arsenic concentrations in multiple water sources using novel boosting algorithms such as natural gradient boosting (NGB), categorical boosting (CATB), and adaptive boosting (ADAB) and compare them with other existing representative boosting algorithms, and (ii) introduce a novel SHapley Additive exPlanation (SHAP) approach for interpreting the performance of ML models. The outcome of this study indicates that the newly introduced boosting algorithms produced efficient performances, which are comparable to the state-of-the-art boosting algorithms and a benchmark random forest model. Interestingly, the extreme gradient boosting (XGB) proved superior over the remaining models in terms of overall and single-class performance metrics measures. Global and local interpretation (using SHAP with XGB) revealed that high pH water is highly correlated with high arsenic water and vice versa. In general, high pH, high Cond, and high TDS were found to be the potential indicators of high arsenic water sources. Conversely, low pH, low Cond, and low TDS were the main indicators of low arsenic water sources. This study provides new insights into the use of ML and explainable methods for arsenic modelling.
Publisher: Elsevier BV
Date: 07-2023
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 05-2022
Publisher: Elsevier BV
Date: 05-2022
Publisher: Elsevier BV
Date: 2022
No related grants have been discovered for Bemah Ibrahim.