Soil classification is essential for understanding soil properties and their suitability for conveying the characteristics of soil types. In this study, we present a prediction of soil classification using fewer soil variables by employing the random forest (RF) technique in machine learning. This study compiled the parameters outlined in the unified soil classification system (USCS), a widely used method for categorizing soils based on their properties and behavior. These parameters, encompassing grain size distribution, Atterberg limits, the coefficient of uniformity, and the coefficient of curvature, were defined within specific ranges to create a synthetic database for training the RF model. The importance of input factors in soil classification was assessed using the out-of-bag samples in RF. Through rigorous validation techniques, including cross-validation, the performance of the RF model is thoroughly assessed, demonstrating its capability to accurately evaluate soil classification. The findings indicate that the RF model presented in this study exhibits a promising alternative, providing automated and accurate classification based on soil data. Notably, the model indicates that the coefficients of uniformity and gradation are insignificant for soil classification and can predict soil types even when these factors are missing, a feat that traditional methods struggle to achieve.

Public transport bus services often experience financial inefficiencies due to high operational costs and unbalanced service allocation. To address these challenges, this study presents a machine learning-based framework aimed at optimizing financial and operational performance in public bus systems. A dataset comprising 57 routes including cost, service, and ridership data was analyzed to identify key factors correlated with net revenue. These features were integrated into multiple predictive models, among which support vector regression (SVR) with a Gaussian kernel and Bayesian optimization achieved the highest accuracy (R² = 0.99), indicating excellent generalization capability. Scenario simulations using the trained SVR model evaluated the effects of service and cost adjustments. Results showed that cutting personnel costs had the most significant effect on net income, followed by administrative and financial expenses. These findings highlight the importance of data-driven strategies such as route reallocation and workforce optimization. The proposed framework offers transit agencies a robust tool for improving efficiency and ensuring financial sustainability.