Water search and rescue (SAR) for ship accidents in China: analysis of 12 years’ data

Huang, Jie; Han, Ning; Jiang, Bi-Qing; Cao, Yan; Han, Xiaotong

doi:10.21037/atm-22-4530

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Zhang et al ( 9 ) have proposed dynamically allocating emergency resources; yet existing models struggle with uncertain data and lack the capability to adapt human resources promptly. The demand for Emergency Medical Services (EMS) at sea fluctuates on a monthly basis, particularly during periods of heightened migrant, refugee, and asylum seeker rescues, or mass casualties from disasters ( 10–13 ). Leveraging historical data to forecast future requirements aids in improved scheduling and staffing, thereby enhancing emergency supply reserves for high-risk days.…”

Section: Introductionmentioning

confidence: 99%

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

Yang,

Cheng,

Zhang

et al. 2024

Front. Public Health

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IntroductionRescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.MethodsIn this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2) as performance metrics.ResultsIn this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA’s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions.DiscussionWhile the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

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Section: Introductionmentioning

confidence: 99%

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

Yang,

Cheng,

Zhang

et al. 2024

Front. Public Health

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“…Generally, the solution complexity of multi-objective MSR problems increases exponentially with the increased number of tasks. Additionally, the previous study [16] pointed out that severe weather is a key factor causing marine accidents. Therefore, the present study aims to propose an advanced algorithm to solve complex multi-objective MSR problems under severe weather conditions.…”

Section: Introductionmentioning

confidence: 99%

An Improved NSGA-II Based on Multi-Task Optimization for Multi-UAV Maritime Search and Rescue under Severe Weather

Huang

et al. 2023

JMSE

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The international trade heavily relies on maritime transportation. Due to the vastness of the ocean, once an accident happens, fast maritime search and rescue (MSR) is a must, as it is of life-and-death matter. Using unmanned air vehicles (UAVs) is an effective approach to completing complex MSR tasks, especially when the environment is dangerous and changeable. However, how to effectively plan paths for multi-UAVs under severe weather, e.g., to rescue the most urgent targets in the shortest time, is a challenging task. In this study, an improved NSGA-II based on multi-task optimization (INSGA-II-MTO) is proposed to plan paths for multi-UAVs in the MSR tasks. In the INSGA-II-MTO, a novel population initialization method is proposed to improve the diversity of an initial population. Further, two tasks are introduced during the execution of the search algorithm. Namely, one assistant task, which solves a simplified MSR problem through multi-task optimization, is implemented to provide necessary evolutional knowledge to a main task that solves an original MSR problem. The performance of the proposed INSGA-II-MTO is compared with other competitors in three MSR scenarios. Experimental results indicate that the proposed algorithm performs best among the compared ones. It is observed that the INSGA-II-MTO can find a set of shorter total paths and handle the most urgent task in the shortest possible time. Therefore, the proposed method is an effective and promising approach to solving multi-UAVs MSR problems to reduce human causalities and property losses.

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Selfish or cooperative? Understanding the behavior of ships during an overtaking process from the spatiotemporal perspective—A case study of Qingdao Port, China

Zhang,

Ma,

Liu

et al. 2024

Ocean Engineering

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Water search and rescue (SAR) for ship accidents in China: analysis of 12 years’ data

Cited by 5 publications

References 28 publications

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

An Improved NSGA-II Based on Multi-Task Optimization for Multi-UAV Maritime Search and Rescue under Severe Weather

Selfish or cooperative? Understanding the behavior of ships during an overtaking process from the spatiotemporal perspective—A case study of Qingdao Port, China

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