An extension to decision tree machine learning model for prediction of the higher heating value of biomass using augmented correlations

This preliminary research article presents an alternative formulation of machine learning (ML) models for the prediction of the higher heating value (HHV) of biomass. The proposed method aims to improve the accuracy of the decision tree model (DT) while preserving its computational performance. It used 21 HHV prediction correlations augmented with the ML model formulation process rather than the sole use of raw experimental data as in a conventional approach. The method was also tested and compared with other two ML models, including support vector machine (SVM) and artificial neural network (ANN), with 148 experimental data sets. The test results showed that integration of the proposed method with DT had 2.72 times higher accuracy than the conventional model based on the R2 criterion with comparative computational performance.