The automatic identification system of maritime accident risk using rule-based reasoning


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Présentation lors de la conférence IEEE SOSE 2012.
Current maritime traffic monitoring systems are not sufficiently adapted to the identification of maritime accident risk. It is very difficult for operators responsible for monitoring traffic to identify which vessels are at risk among all the shipping traffic displayed on their screen. They are overwhelmed by huge amount of kinematic ship data to be decoded. To improve this situation, this paper proposes a system for the automatic identification of maritime accident risk. The system consists of two modules. The first automates expert knowledge acquisition through the computerized exploration of historical maritime data, and the second provides a rule-based reasoning mechanism.

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The automatic identification system of maritime accident risk using rule-based reasoning

  1. 1. Centre de recherche sur les Risques et les CrisesThe automatic identification system of maritimeaccident risk using rule-based reasoning Bilal IDIRI, Aldo NAPOLI Mines ParisTech Centre for Research on Risk and Crises IEEE international conference on System Of Systems Engineering, SOSE’2012
  2. 2. Plan 1 Introduction 1 Context 2 The maritime tracking systems 3 Problematic 4 Background 2 Proposition 1 Hypothesis and research objectives 2 Methodological approach 3 Our modelling approach 3 Application to the risk of maritime accidents 1 The databases 2 Knowledge discovery 3 Automatic identification of risks 4 Conclusion and future works
  3. 3. Introduction
  4. 4. Context  The importance of maritime activity • 90% of international trade, • 80% of energy transport, • 50 million passengers each year in Mediterranean Sea, • 1.19 billion Deadweight tons (dwt) in 2009, 6.7% more compared to 2008 (CNUCED, 2009).  The sea: a complex environment • Existence of international free spaces, • Several coastal states with their own regulations, • Multitude of actors, we are interested • Multiplicity of risks (safety and security) in maritime tracking systems  Several safety and security maritime devices • Organisms responsible for safety and security, • Regulations (ENC, legislative packages Erika I, II, III, etc.), • Systems of navigational aid (NavTrack, Marine GIS, ex-Trem, etc..), • Maritime tracking systems (Spationav, SIVE, SYTAR, etc..) 4Introduction > Proposition > Application > Conclusion
  5. 5. Maritime tracking systems Definition: they allow the retrieval and fusion of information on vessels (position, heading, speed, etc.) for monitoring traffic on a display device. Data acquisition infrastructure Control interface Maritime surveillance operator Source (DenisGouin, 2010)  Maritime risks are still important • Worldwide, there are still many thousands of maritime accidents each year, • 54 700 tonnes of oil and hazardous substances accidentally discharged in 2009 against 7500 tonnes in 2008 (Cedre, 2009) • 445 acts of piracy recorded (+8.5% in one year) and 1181 marine taken hostages in 2010 (BMI, 2010), • … 5Introduction > Proposition > Application > Conclusion
  6. 6. Problematic  In our days, the identification of risks with maritime tracking systems are difficult and complicated. Why ?  The wide area to be monitored,  The amount of ships at sea (41 million ship positions/day for Difficult 62,000 ships according to LIoyds), risk  The multiplicity of scenarios, identification  The risk events that are scattered and fragmented in time and space, 6Introduction > Proposition > Application > Conclusion
  7. 7. Background (1)  Several earlier studies have addressed the issue of improving maritime tracking systems – either at the level of the data acquisition infrastructure or at the information processing level. At the information processing level Improving SM Automatic Risk analysis identification of risks Numerical Probabilistic Statistics Clustering modelling Visualization simulation (Amrozowicz 1996) (Maio and al. 1991), (J. R. W. Merrick and (Torun and Düzgün (Darpa 2005) (Willems and al. 2009; 2011) (Amrozowicz and al. 1997) (LeBlanc and Rucks al. 2000), (J. Merrick 2006) (Morel and al. 2008; 2009, (Riveiro and al. 2008; Riveiro (Kuroda et al. 1982) 1996) and al. 2002) (Marven and al. 2010, 2011) and Goran Falkman 2009; (Chaze and al., 2012) 2007) (Roy 2008; Roy 2010) Riveiro and Göran Falkman (Etienne and al. 2010) 2011) (Gouin and al. 2011; (Cledo 2010) Lavigne and Gouin 2011) (Nilsson and al. 2008) (Laere and Nilsson 2009) (Vandecasteele and Napoli, 2012) (IDIRI and Napoli, 2012) 7Introduction > Proposition > Application > Conclusion
  8. 8. Background (2) Improving SM Automati c Risk identificat analysis ion of  Limitations of this research risks Numerical Probabilis Visualizati Statistics simulatio Clustering modelling tic on n  Maritime knowledge modelling often based on brainstorming (Nilsson et al. 2008) (Roy 2008) and mathematical modelling:  Interesting but complicated and expensive,  The output scenarii rely heavily on the expert knowledge of individuals,  Knowledge previously known by the experts.  Knowledge modelling by data mining little explored (Darpa 2005):  Easy and allows the discovery of new knowledge,  Scalable.  The definition of maritime risk is based on behavior of vessels (abnormal, unusual, etc.) and on zoning.  Abnormal behavior does not necessarily correspond to a risk,  Normal behavior can be a risk. 8Introduction > Proposition > Application > Conclusion
  9. 9. Proposition
  10. 10. Hypothesis and research objectives  There is a need for modelling and automatic identification of maritime risks. Hypotheses  Using new data should allow better analysis of maritime risks,  Using new methods of information processing should enable better identification of risk. Objectives  Propose a new approach to risk modelling based on data mining,  Propose automated identification of maritime risks,  Extend the definition of maritime risk, 10Introduction > Proposition > Application > Conclusion
  11. 11. Methodological approach (1)  Spatial and temporal decision aid problematic:  Monitoring of vessels operating in an open space,  Decision on the riskiness of a ship or not.  Data mining for risk modelling at sea  Definition: “Discovery of interesting, implicit knowledge in spatial databases, is an important task for understanding and use of spatial data and knowledge bases“ (Krzysztof and Han, 1995)  The Spatial Data Mining allows automatic exploration data to search for knowledge. 11Introduction > Proposition > Application > Conclusion
  12. 12. Methodological approach (2)  Automatic reasoning for automatic identification of risks J. Roy (Roy 2010) applied the automated reasoning to automatically identify abnormal behavior of ships.  Automatic reasoning simulates the human reasoning of a machine to infer new knowledge from the input events and knowledge previously stored. Knowledge CBR RBR Knowledge Case Case generation Modularity Problem Rule  Selection of rule-based reasoning: Problem resolution Adapted case Rule application (fast)  Easy to understand, Reasoning Non-deductive Deductive  Modular as rule, Easy (episodic problem Difficult (how to go about resolving a  Deductive reasoning Acquisition solving) problem) 12Introduction > Proposition > Application > Conclusion
  13. 13. Methodological approach (3)  Extending the definition of maritime risks as a combination of three concepts: risk situation, risk behavior and risk area. • Areas of high density of interesting cases (maritime accident, drug trafficking, etc.). Risk area • Behavior of ships describing risk situations (outlier path, slowing, etc.). Risk behavior • Meets of factors suitable for a kind of risk (weather, oceanography, etc..) Risk situation  Complementarities between these three concepts to maximize the chances to identify risks. 13Introduction > Proposition > Application > Conclusion
  14. 14. Our modelling approach (1) 14Introduction > Proposition > Application > Conclusion
  15. 15. Our modelling approach (2) 1 Knowledge Automatic  The first module involves knowledge acquisition from Acquisition historical data Data mining Events flow Generation Operators/experts Knowledge Knowledge base in Interface helps the validation / definition of knowledge 15Introduction > Proposition > Application > Conclusion
  16. 16. Our modelling approach (3) 2 Rule-based Reasoning  The second module uses knowledge in the task of automatic risk identification Knowledge Knowledge Rules engine Applicable 2. Conflict Resolution Selected 1. Match 3. Execution rules rules Facts Facts 16Introduction > Proposition > Application > Conclusion
  17. 17. Application to the risk of maritimeaccidents
  18. 18. The databases  MAIB data:  Historical accidents/incidents between 1991 and 2009,  14,900 accidents and incidents,  16,230 ships.  MERRA data:  Provides meteorological data from the period 1991-2009,  Regular download (1 time/day) to supply the facts.  AIS data:  Historical data since ~ 3 months,  Continuous flow of ship movements. 18Introduction > Proposition > Application > Conclusion
  19. 19. Knowledge discovery (1)  Data mining problems  Associations Rules(Agrawal et al., 1993): is an unsupervised data mining method that allows extracting knowledge rules of the type “if condition then result” from itemsets that frequently appear together in a database (an itemset is a set of items and an item is an instance of a database object).  Spatial Clustering (Zeitouni, 2006): Problem for unsupervised automatic grouping of records (objects) into groups (clusters) as a measure of similarity or distance (domain specific).  Clustering of trajectories (Lee et al., 2007): Clustering whose objects are paths and sub-paths. 19Introduction > Proposition > Application > Conclusion
  20. 20. Knowledge discovery (2) Risk situation Risk area Risk behaviour Spatial and temporal Data mining Spatial data mining data mining Spatial and temporal static data Spatial and temporal (historical) dynamic data (flow) Accidents investigations Moving Environmental data vessels data data 20Introduction > Proposition > Application > Conclusion
  21. 21. Association rules application  Risk situation (Idiri and Napoli, 2012) Accidents and incidents data bases of British ships Apriori algorithm Package Rattle 2.6.4 de R  14,900 accidents  16,230 ships  years 1991-2009 Résultats Prediction rules : {Vessel_Category=Fishcatching/processing}{Incident_Type=Machinery Failure} supp=0.39 ;conf= 0.6 ;lift=1.23. Targeting Rule : {Vessel_Category=Fishcatching/processing}{Vessel_Type=Trawler} supp=0.14; conf=0.43 ; Lift=3. Banal rule : {Vessel_Category=Passenger}{Pollution_Caused=No} supp=0.15; conf=0.73; lift=1.2. 21Introduction > Proposition > Application > Conclusion
  22. 22. Clustering application  Risk area  Methods for density (DBSCAN, OPTICS, etc..)  Automatic identification of the number of clusters,  Cluster with random shapes..  Choice of algorithm OPTICS (Ankerst et al. 1999) Accidents and incidents data bases of British ships OPTICS algorithm Framework Java ELKI 0.4.0 22Introduction > Proposition > Application > Conclusion
  23. 23. Clustering trajectories application  Risk behavior • Discovery of aberrant trajectories (Trajectory Outlier) and especially the sub- trajectories (Outlying sub-trajectory) that do not follow the same trend as the other sub-trajectory. Source (J. Lee, 2008) TRAOD Algorithm (TRAjectory Outlier Detection) (Lee et al. 2008) in 2 steps: 1. Partition: Each trajectory is partitioned into an set of t-partitions, 2. Detect: Outlying t-partitions are identified based on the distance of neighboring trajectories. Source (J. Lee, 2008) 23Introduction > Proposition > Application > Conclusion
  24. 24. Automatic identification of risks  Association rules example Rule {Location = Coastal waters, Vessel_Category = Fishing/processing, Age_Slice_Of_Vessel = 11 to 18 years}  {Incident_Type = Machinery Failure} Measures support = 0.086 confidence = 0.725 lift = 1.47 Interpretation If there is a fishing vessel, aged 11-18 years, sailing in coastal waters then there is a risk that it will break down.  Add rules in Drools (Drools 5.4.0 Jboss Rules) • Add rules : Rules “Risk of mechanical failure” when $s: ship (location= = “Coastal waters”, ship class ==“fishing”, • Add a fact: age ≥ 11, age ≤ 18) Rule “Add Ship Course” then check_behavior ($s.id_ship); When then end insert (new Course ()); Rules “Behavior: drifting of course” end when $r: risk (Type==”Mechanical failure”) course (behavior==”Drifting”) then channel[“alerts”].send(new Alert()); end 24Introduction > Proposition > Application > Conclusion
  25. 25. Conclusion and future works  Results  New approach to the modelling of maritime risks based on data mining,  Real-time identification of maritime risk by automatic reasoning,  Improving the identification of maritime risks by extending the definition of maritime risk.  Future works  Implementing the sub-trajectory clustering on AIS data,  Designing and implementing the interface for definition and validation of knowledge,  Studying the real-time mobile database (frequent updates). 25Introduction > Proposition > Application > Conclusion
  26. 26. ? Centre de recherche sur les Risques et les Crises Thanks for attentionBilal IDIRIPhD student Personal page +33 (0) 4 93 95 75 77 Fax. : +33 (0) 4 93 95 75 81E-mail: :