That is, the support vector machines (SVM) , random forest (RF) , and k-nearest neighbors (KNN) . Evaluation metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2 ) are employed for the performance of these algorithms. MEA and RMSE are both metrics used to evaluate the accuracy of predictions made by an algorithm or model, whereas R 2 quan�fies the extent to which the model's predicted values align with the observed ones . The optimal algorithm performance is characterized by achieving the maximum value of R 2 (0-1) while minimizing the values of MAE and RMSE.