Search Results (3)

In this study, a Hybrid Maritime Risk Assessment Model (HMRA) integrating automated machine learning (AML) and deep learning (DL) with hydrodynamic and Monte Carlo simulations (MCS) was developed to assess maritime accident probabilities and risks. The machine learning models of Light Gradient Boosting (LightGBM), XGBoost, Random Forest, and Multilayer Perceptron (MLP) were employed. Cross-validation of model architectures, calibrated baseline configurations, and hyperparameter optimization enabled predictive precision, producing generalizability. This hybrid model establishes a robust maritime accident probability prediction framework through a multi-stage methodology that ensembles learning architecture. The model was applied to İzmit Bay (in Türkiye), a highly jammed maritime area with dense traffic patterns, providing a complete methodology to evaluate and rank risk factors. This research improves maritime safety studies by developing an integrated, simulation-based decision-making model that supports risk assessment actions for policymakers and stakeholders in marine spatial planning (MSP). The potential spill of 20 barrels (bbl) from an accident between two tankers was simulated using the developed model, which interconnects HYDROTAM-3D and the MCS. The average accident probability in İzmit Bay was estimated to be 5.5 × 10⁻⁴ in the AML based MCS, with a probability range between 2.15 × 10⁻⁴ and 7.93 × 10⁻⁴. The order of the predictions’ magnitude was consistent with the Undersecretariat of the Maritime Affairs Search and Rescue Department accident data for İzmit Bay. The spill reaches the narrow strait of the inner basin in the first six hours. This study determines areas within the bay at high risk of accidents and advocates for establishing emergency response centers in these critical areas. Full article

Submarine pipelines have become integral for transporting resources and drinking water across large bodies. Therefore, ensuring the stability and reliability of these submarine pipelines is crucial. Incorporating climate change impacts into the design of marine structures is paramount to assure their lifetime safety and serviceability. Deterministic design methods may not fully consider the uncertainties and risks related to climate change compared to risk-based design models. The latter approach considers the future risks and uncertainties linked to climate and environmental changes, thus ensuring infrastructure sustainability. This study pioneers a Hybrid 3D Hydrodynamic Monte Carlo Simulation (HMCS) Model to improve the reliability-based design of submarine pipelines, incorporating the effects of climate change. Current design approaches may follow deterministic methods, which may not systematically account for climate change’s comprehensive uncertainties and risks. Similarly, traditional design codes often follow a deterministic approach, lacking in the comprehensive integration of dynamic environmental factors such as wind, waves, currents, and geotechnical conditions, and may not adequately handle the uncertainties, including the long-term effects of climate change. Nowadays, most countries are developing new design codes to modify the risk levels for climate change’s effects, such as sea-level rises, changes in precipitation, or changes in the frequency/intensity of winds/storms/waves in coastal and marine designs. Our model may help these efforts by integrating a comprehensive risk-based approach, utilizing a 3D hydrodynamic model to correlate diverse environmental factors through Monte Carlo Simulations (MCS). The hybrid model can promise the sustainability of marine infrastructure by adapting to future environmental changes and uncertainties. Including such advanced methodologies in the design, codes are encouraged to reinforce the resilience of maritime structures in the climate change era. The present design codes should inevitably be reviewed according to climate change effects, and the hybrid risk-based design model proposed in this research should be included in codes to ensure the reliability of maritime structures. The HMCS model represents a significant advancement over existing risk models by incorporating comprehensive environmental factors, utilizing advanced simulation techniques, and explicitly addressing the impacts of climate change. This innovative approach ensures the development of more resilient and sustainable maritime infrastructure capable of withstanding future environmental uncertainties. Full article

Sea outfall systems are preferred to refinery systems because of the assimilation capacity of the sea as an economical choice. If sea outfall systems are chosen, the location of the sea outfall is critical for preventing the return of wastewater to the coastal zone and recovery back into an ecosystem. On the basis of the regulation of water pollution control, bacterial concentration needs to be below a certain value in the protected area. The primary effects on dilution are coastal currents generated by wind and transport of wastewater in closed or semi-closed coastal regions, as found in Turkey. Accurate predictions of wind and wave climates and currents are critical in sea outfall planning. In this study, the wind climate is determined from the data provided by the Edremit and Ayvalık Meteorological Stations and European Centre for Medium-Range Weather Forecasts operational archive at the coordinates of 39.50° N–26.90° E. Wind, wave, and current roses are prepared by HYDROTAM-3D. CORMIX was used for the near-field dilution, and HYDROTAM-3D, a three-dimensional hydrodynamic transport model, was used for the far-field dilution of the pollutant. The results of near-field and far-field dilution modeling show that the sea outfall of Edremit–Zeytinli meets the legal regulations. Full article