An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost

He, Jian; Hao, Yong; Wang, Xiaoqiong

doi:10.3390/jmse9020156

Cited by 18 publications

(12 citation statements)

References 32 publications

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“…Since its release in 2014, XGBoost has been a very popular machine learning method, and it has a highly impressive winning record when it comes to machine learning competitions. XGBoost has already been used in several transportation risk assessment applications both within road traffic [48,58,57], aviation [39], and shipping [18,25,3].…”

Section: Extreme Gradient Boostingmentioning

confidence: 99%

A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data

Midtfjord,

De Bin,

Huseby

2021

Preprint

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The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry during the winter seasons. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classification model to predict slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and data from runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. To evaluate the performance of the models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy when domain knowledge is used in the variable extraction. The XGBoost models are combined with SHAP (SHapley Additive exPlanations) approximations to provide a comprehensible decision support system for airport operators and pilots, which can contribute to safer and more economic operations of airport runways.

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Section: Extreme Gradient Boostingmentioning

confidence: 99%

A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data

Midtfjord,

De Bin,

Huseby

2021

Preprint

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“…Yuan et al [1] investigated the factors influencing the implementation of ship selection methods for the PSCO through an analytical hierarchy process. He et al [12] proposed a novel interpretable ship detention decision-making model based on machine learning for flag state control. The model adopted the extreme gradient boosting and synthetic minority oversampling technique algorithms to identify whether a ship should be detained.…”

Section: Psc Related Researchmentioning

confidence: 99%

“…Therefore, given that the deficiencies of ships may affect the analysis results, this study believes that MCDM is more appropriate. Subsequent research results by many researchers proved that the MCDM model applied to the research topic of PSC screening of inspected ships has significant effects [11][12][13]. Through the MCDM model, their research results have achieved good results in simulating the screening of inspected ships.…”

Section: Introductionmentioning

confidence: 99%

“…With the unceasing increase in the amount of data, the data processing technique can enable more scientific and efficient screening of information and processing of the database [10]. This allows the data processing technique of potential regularities in ship detention deficiencies that have not yet been noticed [4,11,12]. In addition, when performing data analysis, it is not possible to directly analyze the 18 deficiency categories of ships, as classified by PSC.…”

Section: Introductionmentioning

confidence: 99%

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Ship Deficiency Data of Port State Control to Identify Hidden Risk of Target Ship

Shen

Liu

Chang

et al. 2021

JMSE

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In the new inspection regime (NIR) of port state control (PSC), the criteria for being judged as a standard risk ship (SRS) is too broad. Some ships are classified as SRS even though they have a large number of ship deficiencies. This paper develops a selection system to identify the hidden risk of target ships in the SRS category using PSC inspection records. This system allows the target ship to be used to help reduce cases of flags being greylisted or blacklisted, which can cause huge shipping losses. This study analyzes ship deficiency data in the Tokyo memorandum of understanding (Tokyo MoU) database. It adopts the multiple criteria decision making (MCDM) model as a data processing technique to build a risk assessment scale. It uses fuzzy importance performance analysis (F-IPA) and technology for order preference by similarity to the ideal solution (TOPSIS) for its analysis. Subsequently, the weights of F-IPA and TOPSIS are adopted into the MCDM model. This article also consulted the Tokyo MoU database. It has been verified that the next time PSC inspection, the system hits 83.3% of the hidden risk ships in the SRS category. Thus, this system will help inspectors be more insightful for target ships.

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“…However, the losses caused by marine accidents are always enormous, e.g., loss of life, economic and environmental pollution, etc. [2,3]. The research of autonomous ships is considered one of the effective solutions.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

Zhang

Mou

Chen

et al. 2021

JMSE

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In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

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An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost

Cited by 18 publications

References 32 publications

A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data

A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data

Ship Deficiency Data of Port State Control to Identify Hidden Risk of Target Ship

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

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