# Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review
## 3 Machine Learning in Supply Chain Management of Biodiesel  
Traditionally, mathematical modeling has been used as a tool to evaluate the performance of the supply chain of biodiesel [27]. However, over the decades, the number of studies applying machine learning technology to biodiesel has increased, because machine learning offers a promising alternative to conventional modeling methods dealing with the nonlinear and complex characteristics of the biodiesel supply chain [37]. Machine learning(ML), one of the types of artificial intelligence, has been widely recognized as a component of the Fourth Industrial Revolution in different scientific fields, including bioenergy [38]. It is a technology that enables computers to learn and make decisions without explicit programming [39]. Also, it provides a set of advanced data analysis methods based on mathematical, probability, and statistical principles that identify patterns among features within a dataset and enable prediction [40].  
Nine popular machine learning algorithms used for the supply chain management of biodiesel are the Gaussian process model (GPM), random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (KNN), AdaBoost regression, multiple linear regression (MLR), linear regression (LR), and multilayer perceptron (MLP). These nine algorithms are applied to feedstock yield estimation, biodiesel productivity prediction, and biodiesel quality prediction.  
GPM refers to a set of random variables that when considered together follow a multivariate Gaussian distribution [41]. Also, it provides a robust Bayesian framework to effectively formulate an approximation problem, and it has been proven to provide predictions with good accuracy for geophysical studies [42]. RF is an ensemble of tree predictors, where each individual tree relies on the outcome of a random vector independently sampled and having an identical distribution for every tree within the forest. It has the advantage of preventing overfitting through the bagging process, which can improve prediction accuracy [43]. ANN is operated as an effective solution for modeling complex and poorly defined problems, and it is usefully applied to pattern recognition and a target value estimation [44]. SVM, which was first introduced by [PERSON] and [PERSON], originated from statistical learning theory and the concept of minimizing structural risk [45]. Initially used to solve classification, it was successfully developed over time and used for regression [41]. Recently, it has been implemented for classification, pattern recognition, and the analysis of regression due to it outperforming other methodologies [46]. KNN is an algorithm that was initially used in statistical applications. The fundamental idea of KNN is to identify a set of \(k\) samples from the training dataset that are closest to a given unknown sample [47]. In KNN, the \(K\) parameter, indicating the count of the nearest neighbors, plays an important role in determining the performance of the classifiers [48]. The AdaBoost algorithm was initially proposed by [PERSON] and [PERSON] as an approach to creating a robust classifier from a collection of weak classifiers [49]. Weight, indicating relative importance, is allocated during each training. Instances that were incorrectly classified by the last iteration are given greater weights, which are adjusted after each iteration [50]. MLR is a complex version of the simple LR designed for the dataset that involves multiple predictor variables and outcome variables [51]. MLP stands out as one of the most recognized ANN models. Due to its good capacity for nonlinear mapping, it has been widely utilized in pattern recognition, image processing, fusion approximation, and optimization computation [52]. The following sections address applications of these nine algorithms.