# Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review
## 4 Discussion  
In Section 3, applications of machine learning (ML) algorithms, feedstock yield prediction, biodiesel productivity prediction, and biodiesel quality prediction were investigated. Nine machine learning algorithms, namely, GPM, RF, ANN, SVM, KNN, AdaBoost regression, MLR, LR, and MLP, were applied to feedstock yield estimation, biodiesel productivity prediction, and biodiesel quality prediction. Although there are multiple machine learning algorithms, there exists a general machine learning procedure consisting of data collection, machine learning with a training dataset, model evaluation with a test dataset, and model validation. The validated model can be utilized to estimate values of a dependent variable (e.g., feedstock yield, biodiesel productivity, and biodiesel quality) under a designed scenario (e.g., a climate change scenario).  
In feedstock yield prediction, supervised machine learning algorithms involving RF, GPM, ANN, and SVM are frequently used with historical climate data (e.g., temperature, precipitation, crop evapotranspiration, real crop evapotranspiration, storage, deficit, and surplus). Among the four ML algorithms, RF is selected as the best-performing algorithm.  
Similarly, RF is also widely used in biodiesel productivity prediction with high prediction accuracy. [PERSON] et al. (2019) found that RF showed better performance than KNN, SVM, and AdaBoost regression to predict biodiesel production quantity from five independent vari  
Figure 5: Modeling procedure of ANN.  
ables (i.e., catalyst loading (wt.%), feedstock type, molar ratio of methanol to oil, reaction time (min), and reaction temperature (\({}^{\circ}\)C)). On the other hand, KNN [60] also provided high prediction accuracy, with an \(R^{2}\) value of 0.8554 among LR, MLP, and KNN under three independent variables of reaction duration, catalyst concentration, and methanol quantity/molar ratio. Unlike LR, KNN does not require any correlations between variables, so it enables the development of more flexible models than LR. However, the performance of KNN is heavily dependent on the selection of the \(K\) parameter, which may result in unreliable performance in different datasets [48].  
In biodiesel quality prediction, ANN and MLR were widely utilized to predict the density, dynamic viscosity, flash point, higher heating value, oxidative stability, and cetane number [63; 65]. Because there are multiple factors influencing the quality of biodiesel, the ML algorithms involving multivariate inputs are mainly utilized, and correlation analysis was simultaneously conducted. Traditional statistical modeling algorithms, such as LR and MLR, provided lower prediction accuracy than other machine learning algorithms that enable nonlinear relationships between variables to be explained [66]. Nevertheless, those statistical approaches can identify significant variables (or predictors) in the prediction model. Considering that machine learning algorithms are recognized as a black-box approach [67], the limitation can be resolved by integrating the advantages of multiple algorithms.