Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
Abstract
:1. Introduction
2. Materials and Methods
2.1. Database and Codification
2.2. ANOVA
2.3. Clustering
2.4. Neural Networks
3. Results and Discussion
3.1. Database
3.2. Identification of Principal Variables
3.2.1. ANOVA Analysis
3.2.2. Random Forest Variables
3.3. Clustering
3.3.1. Clusters 1 and 2
3.3.2. Clusters 6 to 8
3.4. Neural Networks
4. Conclusions
- The main variable related to maritime accidents in the SAR region is the enforcement of the minimum crew members and, at a lower level, the ship dimensions, which, indirectly, are associated with the ship type.
- The results are centred in the SAR region, and this original methodology must be tested in another region where weather conditions and new variables like crew members’ experience can affect the selection of the more relevant variables.
- Three main clusters can be observed in Figure 6, Figure 7, Figure 8 and Figure 9 based on their three main colours (cluster 1 (in white), cluster 2 (light green) and cluster 3 (dark green), which are associated with the meeting of the minimum crew member requirement and that are differentiated by the year of construction of the ship. At the same time, based on the 3D model, it can be concluded that a more adequate definition of the minimum number of crew members may help to decrease the number of accidents.
- It is possible to obtain a mathematical model based on feedforward neural networks that let us predict the expected type of accident at any moment based on the year of construction, wind force, nighttime, length, beam and minimum crew with a determination factor over 67%, and new variables associated with the human factor could increase this result in future works.
- An electronic control system based on this model can be of interest to predict and prevent future maritime accidents in this region.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Code | Ship Type |
---|---|
1 | Extraction Platforms and Auxiliary Supply vessels |
2 | Passenger and/or cargo vessels |
3 | Fishing vessels |
4 | Fishing and Aquaculture Auxiliary Boats |
5 | Tugboats and auxiliary boats of the Port Service |
6 | Commercial Yachts |
7 | Recreational Boats |
8 | Vessels of Public Organizations (National or Autonomous Communities) |
9 | Provisional List of Vessels under Construction |
Code | Type of Accident |
---|---|
1 | Contact |
2 | Flooding |
3 | Grounding |
4 | Collision |
5 | Fire |
6 | Capsizing |
7 | List |
8 | Stranded |
9 | Loss of Stability |
10 | Operational Accident |
Code | Denomination | |
---|---|---|
One ship involved | Two ships involved | |
1 | Meets | - |
2 | Do not meet | - |
3 | - | One does not meet |
4 | - | Two do not meet |
5 | - | Two meet |
Code | Visibility Level |
---|---|
1 | Very Bad (less than 1000 m) |
2 | Bad (between 1000 m and 2 nautical miles) |
3 | Moderate (between 2 nautical miles and 5 nautical miles) |
4 | Good (more than 5 nautical miles) |
Code | Period of Time |
---|---|
1 | Day |
2 | Night |
Code | Human Factor Conditions |
---|---|
1 | Human factor influences the accident |
2 | Human factor does not influence the accident |
3 | Doubtful |
Wind Force (Code) | Denomination | Velocity | |
---|---|---|---|
(km/h) | Knots | ||
0 | Calm | 0–2 | Till 1 |
1 | Light air | 2–6 | 1–3 |
2 | Light breeze | 7–11 | 4–6 |
3 | Gentle breeze | 12–19 | 7–10 |
4 | Moderate breeze | 20–29 | 11–16 |
5 | Fresh breeze | 30–39 | 17–21 |
6 | Strong breeze | 40–50 | 22–27 |
7 | High wind | 51–61 | 28–33 |
8 | Gale | 62–74 | 24–40 |
9 | Strong gale | 75–87 | 41–47 |
10 | Storm | 88–101 | 48–55 |
11 | Violent storm | 102–107 | 56–63 |
12 | Hurricane force | >118 | >64 |
Sea Conditions (Code) | Douglas Scale | Wave Height | Beaufort Scale | |
---|---|---|---|---|
(m) | (ft) | |||
0 | Calm (Glassy) | No wave | 0 | |
1 | Calm (rippled) | 0–0.10 | 0.00–0.33 | 1–2 |
2 | Smooth | 0.10–0.50 | 0.33–1.64 | 3 |
3 | Slight | 0.50–1.25 | 1.6–4.1 | 4 |
4 | Moderate | 1.25–2.50 | 4.1–8.2 | 5 |
5 | Rough | 2.50–4.00 | 8.2–13.1 | 6 |
6 | Very rough | 4.00–6.00 | 13.1–19.7 | 7 |
7 | High | 6.00–9.00 | 19.7–29.5 | 8–9 |
8 | Very high | 9.00–14.00 | 29.5–45.9 | 10–11 |
9 | Phenomenal | 14.00+ | 45.9+ | 12 |
Ship Type | Accident | Year | Wind Direction | Wind Force | Sea | Visibility | Day/ Night | Length | Beam | Gt. M | Crew | Minimum Crew | Human Factor Influence |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 6 | 1985 | 203 | 4 | 3 | 3 | 1 | 5 | 2 | 1 | 2 | 1 | 1 |
3 | 8 | 1967 | 338 | 3 | 2 | 3 | 1 | 5 | 2 | 1 | 1 | 1 | 0 |
3 | 6 | 1991 | 338 | 5 | 2 | 2 | 1 | 5 | 2 | 1 | 2 | 1 | 1 |
3 | 4 | 1994 | 180 | 3 | 2 | 4 | 2 | 5 | 2 | 1 | 1 | 2 | 0 |
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Ship type | 0.029 |
Year of construction | 0.531 |
Wind force | 0.000 |
Sea conditions | 0.000 |
Swell | 0.000 |
Visibility | 0.584 |
Night | 0.003 |
Length | 0.000 |
Beam | 0.000 |
Cause of the accident | 0.000 |
Number of crew members | 0.732 |
Comply minimum crew members | 0.000 |
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Maceiras, C.; Cao-Feijóo, G.; Pérez-Canosa, J.M.; Orosa, J.A. Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors. Appl. Sci. 2024, 14, 7239. https://doi.org/10.3390/app14167239
Maceiras C, Cao-Feijóo G, Pérez-Canosa JM, Orosa JA. Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors. Applied Sciences. 2024; 14(16):7239. https://doi.org/10.3390/app14167239
Chicago/Turabian StyleMaceiras, Candela, Genaro Cao-Feijóo, José M. Pérez-Canosa, and José A. Orosa. 2024. "Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors" Applied Sciences 14, no. 16: 7239. https://doi.org/10.3390/app14167239