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A comprehensive survey on the role of explanation in artificial intelligence: a case study on prediction of gross calorific value of coal

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  • Sindhu P. Menon

Abstract

The study presented here could act as a basis for researchers interested in learning about essential components of the nascent and quickly developing field of research on explainable artificial intelligence (XAI). SHAP-Xgboost is applied to show the working principle of XAI. This is archived by analysing the coal content in the coal reserves. SHapley Additive explanations will be proposed as a revolutionary XAI for this aim. SHAP allows users to understand the extent of relationships between each unique input data along with its corresponding output, as well as rank input variables in order of efficacy. SHAP was combined with extreme gradient boosting (xgboost) (SHAP-Xgboost) which is one of the latest technological developments. SHAPXgboost was able to model GCV accurately (R2 = 0.99) using proximate and ultimate analysis (chemical content in coal) from the coal samples. These significant discoveries pave the way for the development of high-interpretability algorithms to learn coal properties and point out crucial variables.

Suggested Citation

  • Sindhu P. Menon, 2026. "A comprehensive survey on the role of explanation in artificial intelligence: a case study on prediction of gross calorific value of coal," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 22(1), pages 25-58.
    • Handle: RePEc:ids:ijcist:v:22:y:2026:i:1:p:25-58
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