Maritime accidents such as capsizing, storm-induced roll resonance, collisions and groundings continue to occur in Bangladesh’s inland and coastal waterways. While these events are usually linked to overloading, weather conditions, and maintenance issues, another important hydrodynamic factor - the Added Mass Coefficient (AMC) is rarely examined. Traditional methods for calculating AMC are too slow for use in real operations. In this study, we explored how different machine learning (ML) models, including Random Forest (RF), Neural Networks (NN), Support Vector Regression (SVR), Linear Regression (LR), and Long Short-Term Memory (LSTM) networks can predict AMC values from basic vessel parameters. Our results show that AMC can be considered not only as a design variable but also as an operational safety parameter. By predicting AMC in advance the models provide a way to support safety actions such as adjusting heading, controlling load distribution and reducing risks in shallow-water navigation. We also suggest that future work should combine real-time data with hybrid approaches to strengthen the reliability of predictions.
Liquefied natural gas (LNG) storage tanks are susceptible to thermal stratification, a phenomenon that triggers rollover events and uncontrolled boil-off gas (BOG) generation, posing significant safety and economic risks. Traditional computational fluid dynamics (CFD) approaches offer mechanistic insight but require prohibitive computational resources and precise boundary conditions that are difficult to obtain under industrial operating conditions. Empirical correlations, while efficient, fail to capture the nonlinear, transient coupling between thermal stratification and BOG dynamics. This study presents an integrated experimental and machine learning framework for real-time prediction of BOG rates and rollover risk in a 1.0 m diameter LNG storage tank. A comprehensive dataset comprising 300 operational records was acquired over 70 h. Seven machine learning models were evaluated, and an ensemble of Random Forest, Gradient Boosting, and Multi-Layer Perceptron achieved the highest accuracy (
R
2
= 0.90, RMSE = 0.11 kg/h, MAPE = 3.30%), outperforming both empirical Chato correlations (
R
2
= 0.62) and CFD-RANS simulations (
R
2
= 0.78). Permutation importance revealed that the thermal stratification index and liquid level are the dominant drivers. Bootstrap 95% prediction intervals provided rigorous uncertainty quantification. Feature ablation confirmed that removing the stratification index degrades
R
2
by 0.312. Time-series forecasting yielded
R
2
= 0.968, with Bland–Altman analysis confirming clinical-grade agreement. These findings demonstrate that interpretable, low-cost ensemble learning can serve as a viable digital-twin alternative to first-principles modeling for industrial LNG storage monitoring.
The fast pace of development of e-commerce has elevated the timely delivery as a characteristic element of customer satisfaction and logistics performance. However, there are still delays in shipment because of uncontrollable factors like traffic, weather, operational bottlenecks and network inefficiencies. To overcome this issue, this paper derives a Machine Learning Model of Shipment Delay Prediction to combine refined shipment data, operational time and contextual logistics data to predict the probability of delay at an early phase. Based on the previous studies of real-time delay prediction, proactive risk assessment, as well as ML-based logistics optimization, the suggested framework will integrate feature engineering, supervised learning models (Random Forest, XGBoost, CatBoost, Logistic Regression), and a multi-stage prediction process. This methodology is focusing on interpretability, prediction on each shipment processing step, and scalability to the logistic operations. The experimental findings indicate that the gradient-boosting models are rather consistent in terms of their performance (high ROC-AUC scores and higher recall in the delay class). This study adds a useful and empirical methodology, which can be adopted by logistics teams to predict disruptions, make sound-informed routing, and enhance service reliability.
Pranjul Vishwari, Rajiv N Thakker, Sumit Verma et al.· International Conference on...· 0 citations
Within the framework of the International Maritime Organization (IMO) Second-Generation Intact Stability Criteria, parametric roll (PR) is recognized as an important dynamic stability failure mode that may occur when ships operate in waves. Efficient surrogate approximation of the PR response is therefore relevant to stability assessment under the loading and environmental conditions represented in the database. This study develops a LightGBM-based surrogate model for estimating the COMPASS-calculated parametric-roll response of a large container ship. A COMPASS-generated numerical dataset covers 36 retained loading conditions and includes five loading-condition parameters—displacement, LCG, VCG, RMI, and Cm—together with ship speed, SWH, and AZP. The regression target is the parametric-roll amplitude calculated by COMPASS for each parameter combination. The predictive performance of LightGBM is evaluated using the coefficient of determination and root mean square error and is compared with that of selected benchmark machine learning algorithms. On the held-out loading-condition test set, LightGBM achieves a higher coefficient of determination and a lower root mean square error than the comparison models, indicating favorable performance within the investigated condition space. SHapley Additive exPlanations (SHAP) are further employed to quantify the contribution of individual input features and to examine the relationships between the governing parameters and the predicted parametric roll response. The identified feature effects are generally consistent with the expected physical relationships associated with ship roll behavior. These results demonstrate the feasibility of using LightGBM as an interpretable surrogate for COMPASS-calculated parametric-roll responses within the investigated numerical dataset. Further validation using additional ship types, operating conditions, and experimental or full-scale data is required before the model can be applied more broadly in engineering practice.
River water level prediction plays an important role in effective planning and flood risk mitigation. In this study, four standalone machine learning (ML) models, M5Rules, Random Forest (RF), Sequential Minimal Optimization (SMO), and Long Short-Term Memory (LSTM), as well as a hybrid LSTM-RF model, were developed to predict weekly water levels of the Rhine River. The models were trained and tested using data collected between 2006 and 2024. Different scenarios with different input combinations were explored to improve the accuracy of the model. Statistical indicators were calculated to examine the reliability of the proposed scenarios and models. The results showed that the performance of the model increased in Scenario 4 with all input variables. Among the standalone models the M5Rule and SMO algorithms perform better with Nash-Sutcliffe Efficiency (NSE) of 0.78, in validation phase, followed by RF (NSE = 0.76) and LSTM (NSE = 0.72). In order to increase the model predictive power, the hybrid model LSTM-RF applied to the input variables of the best scenario and this hybrid model achieved a remarkable accuracy of NSE = 0.98 significantly outperforming standalone models. The findings of this research demonstrated the efficacy of the hybrid LSTM-RF model in capturing the changes in the water level in Rhine River.
Zohreh Sheikh Khozani, Monica Ionita· Water resources management· 0 citations
In shale gas development, Net Present Value (NPV) and Internal Rate of Return (IRR) are influenced by the coupling of multi-source geological and engineering parameters, and quantitative research on the marginal effects and risk thresholds of key parameters remains lacking. A deep feedforward neural network prediction model was constructed to achieve mapping from 19-dimensional features to NPV and IRR. The trained model was then utilized as a digital surrogate model to conduct univariate sensitivity analysis, quantifying the marginal impacts of parameters such as clay content, carbonate content, effective porosity, Poisson’s ratio, horizontal stress difference, and first-year average daily production on economic benefits. Cross-validated results indicate that the model achieves a mean R
2
of 0.6071 (±0.0957) for NPV prediction and 0.4168 (±0.1294) for IRR prediction. Furthermore, it identifies economic risk thresholds including 30% for clay content, 0.2 for Poisson’s ratio, and 17 MPa for horizontal stress difference, which are highly consistent with oilfield engineering experience. SHAP-based interaction analysis reveals that these thresholds are context-dependent, with interaction effects accounting for approximately 30%–32% of the main effects for clay content and horizontal stress difference.
Dong Wang, Kai-Xiang He, Huan Cui et al.· Frontiers in Earth Science· 0 citations
Maritime collision risk assessment is essential for ensuring navigational safety under increasingly congested traffic conditions. In such contexts, autonomous vessels must evaluate risks efficiently and respond in real time, which remains challenging when using conventional methods. The widely used Dempster-Shafer (D-S) model, although effective in theory, suffers from high computational complexity and limited scalability when applied to multi-vessel encounters involving both Maritime Autonomous Surface Ships (MASS) and conventional vessels. To address this issue, a machine learning-based framework is proposed, in which an Extreme Gradient Boosting (XGBoost) model replaces the theoretical D-S model for Collision Risk Indicator (CRI) estimation. Automatic Identification System (AIS) data are used to construct realistic simulation scenarios, and the predicted CRI is continuously evaluated and integrated into an automatic collision avoidance algorithm. The proposed model achieves an R² of 95.36% during training and 90.21% in real-world simulation testing. In high-traffic scenarios involving more than 20 vessels, it demonstrates significantly faster processing speed than the D-S model. Safety analysis further shows that integrating CRI with the Velocity Obstacle (VO) algorithm reduces collision risk by 33.0% in autonomous-autonomous vessel encounters and 28.7% in autonomous-conventional vessel encounters. These results indicate that the proposed method supports efficient and scalable real-time collision risk management for autonomous maritime navigation.
Linna Li, Lingyu Zhang, Seyed Parsa Parvasi et al.· WMU Journal of Maritime Affa...· 0 citations