Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
Abstract
:1. Introduction
1.1. ICT and Maritime Data Engineering
1.2. Factors That Raise Marine Traffic Engineering Awareness
1.2.1. Sea Traffic
1.2.2. Accidents and Hazards
Lack of Designated Shipping Lanes
1.2.3. Impact of Maritime Accidents
1.3. Research Questions
- RQ1: How to incorporate various heterogeneous data sources about marine traffic, vessel information and sea conditions towards modeling and understanding potential hazardous situations.
- RQ2: How to exploit historical data towards creating a simulation tool for inferring about future situations under conditions of uncertainty.
- RQ3: How to create useful visualization and insights for the domain, in order to help experts formulate policies, regulations, reform emergency plans, etc.
2. Related Work
3. Theoretical Background
- VesselType:
- ○
- New
- ○
- Old
- Cargo:
- ○
- Dangerous
- ○
- Not Dangerous
- Accident:
- ○
- Yes
- ○
- No
- Pollution:
- ○
- High
- ○
- Low
- The variables VesselType and Cargo are marginally independent, but when Accident is observed (given) they are conditionally dependent. The type of this relation is often named as explaining away.
- When Accident is given, Pollution is conditionally independent of its ancestors VesselType and Cargo.
- Instead of factorizing the joint distribution of all variables using the chain rule, such as P(V,C,A,P) = P(V)P(C|V)P(A|C,V)P(P|A,C,V) the BN defines a compact JPD in a factored form, such as P(V,C,A,P) = P(V)P(C)P(A|V,C)P(P|A). Note that the BN structure reduces the number of model parameters (i.e., the number of rows in the JPD table) from 24 − 1 = 15 to only 8. This property is of major importance since it allows researchers to create a tractable model of domains with a plethora of attributes.
Hybrid Bayesian Networks
4. Marine Simulation System
4.1. Data Description and Preprocessing
- Number of ships
- ○
- It represents the total number of ships that passed through that RoI in a whole time slot (day, week, month)
- Average speed
- ○
- For each RoI, the speed of each instance is averaged on the number of instances
- Hazardous cargo
- ○
- the most frequent hazard level, as extracted by looking at all cargos from ships that passed through this RoI (i.e., nominal value from A to D, A denoting the most hazardous situation). This is a weighted value in order to give more emphasis on the hazardous load.
- Ship density
- ○
- The average distance between all ships that passed through a RoI.
- Wave height
- ○
- As retrieved from the POSEIDON system
4.2. System Programming Details
4.3. Simulation Phase
5. Experimental Evaluation
5.1. Measuring Clustering Validation
5.2. Evaluation of the Inference Performance
5.2.1. Mean Absolute Error
5.2.2. Root Mean Squared Error
6. Modeling Marine Transportation as Social Network
6.1. Social Network Analysis Measures
6.2. Applying SNA to Marine Traffic Data
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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k-Means | DBSCAN | ΕΜ | |
---|---|---|---|
Day | −748.720 | −3582.824 | −1705.336 |
Week | −769.273 | −3701.001 | −1715.009 |
Month | −991.182 | −3717.501 | −2195.938 |
k-Means | DBSCAN | ΕΜ | |
---|---|---|---|
Day | 0.741 | 0.343 | 0.610 |
Week | 0.733 | 0.311 | 0.606 |
Month | 0.681 | 0.303 | 0.588 |
Null Hypothesis: Both Forecasts Have the Same Accuracy | |||
---|---|---|---|
p-value | LR | BN | NN |
LR | 0.0073 | 0.6311 | |
BN | 0.0088 | ||
NN | |||
DM statistic | LR | BN | NN |
LR | 0.6264 | 0.4305 | |
BN | −3.8733 | ||
NN |
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Maragoudakis, M. Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data. Algorithms 2019, 12, 27. https://doi.org/10.3390/a12010027
Maragoudakis M. Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data. Algorithms. 2019; 12(1):27. https://doi.org/10.3390/a12010027
Chicago/Turabian StyleMaragoudakis, Manolis. 2019. "Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data" Algorithms 12, no. 1: 27. https://doi.org/10.3390/a12010027