This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM) which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.
With the development of shipping science, improvement of vessel technology, and the rising trend of multinational manning of ships, maritime safety and marine pollution prevention have been the hot spots in study of the maritime field by all the countries. Against this background, the focuses of maritime safety research are mainly on two aspects. One is the reasonable use of marine technology resources, which is termed as the seamanship. The other is the proper training of the communication and cooperation in the sailing team, which is named as the teamwork in the ship.
In fact, the good seamanship and the excellent teamwork in the deck crew management of the ship are both the most important factors to avoid the vessel accidents. The previous researches about the ship accidents show that more than 80% accidents are caused by human errors, which are inseparable with the level of seamanship and the teamwork. Therefore, in 2010, the IMO announced the sweeping amendments of the STCW convention, named STCW convention Manila amendments. In the convention, marine resource management ability, which is the collectivity name of seamanship and the teamwork, becomes one of the mandatory requirements, and both bridge resource management (BRM) and engine resource management (ERM) are proposed formally. However, STCW Manila amendments only explain the marine resource management qualitatively. So far, a few literatures focus on the data analysis between the vessel accidents and items of BRM. Paper [
Generally speaking, a large amount of maritime accident data can be collected from the actual vessel accident; association rule mining, which is a kind of data mining, is a feasible method to analyze this data. During recent years many researches about the association rule mining are proposed [
This paper mainly researches BRM. Through the analysis of the association rules between items of BRM and vessel accidents, the importance of BRM are evaluated quantitatively and the relationship of its items are mined. The contributions of this paper are
The rest of this paper is organized into the following sections. In Section
In this paper, we analyze items of BRM based on immune genetic algorithm. The goal is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, the cross level coding scheme needs to be proposed. In this section, a multilevel association rule mining will be introduced, where the basic data is considered as the bottom of the data mining, and the summarized data, that is, items of BRM in this paper, is the middle level, which has certain corresponding relationship with the basic data.
Association rule is the hiding relationship among the data items, which is the relevance of different items appearance in the same event [
In our work, for a hierarchical organization to work effectively and feasibility, numbers of items in database are summarized into class items as the higher level of the information, which are interested by the user. Then the items
Mapping relationship of items and class items.
Because the immune system in the process of evolution is the uncertain one which is composed by antibodies, the irregular degree of the immune system can be presented by average information entropy of Shannons.
Assuming an immune system is consisted with
In order to explain the cross level coding scheme, an example of cross level coding from the basic antibody to antibody is shown as follows. The two complete basic antibodies are given in Table
After the cross level coding, the mining of association rules can be executed on the encoded population based on the basic data. However, frequent patterns meeting the support requirements need to be found for association rule mining. When there are multiply levels and dimensions, lots of thresholds should be defined. Meanwhile, the search space, storage space, and running time will increase with the increasing of the complexity of algorithm [
An example of basic antibody.
Basic antibody |
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1 | 1 | 0 | 0 | 0 | 1 |
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0 | 1 | 1 | 0 | 1 | 1 |
The corresponding antibody of Table
Antibody |
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11 | 00 | 10 |
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11 | 00 | 11 |
An example of mapping relationship with 6 items and 3 class items.
Genetic algorithm (GA) is a search heuristic that mimics the process of natural selection, which generates solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In GA, a population of candidate solutions to an optimization problem is evolved towards better solutions. Each candidate solution has a set of properties which can be mutated and altered. The evolution usually starts from a population of randomly generated individuals and is an iterative process, with the population in each iteration called a generation. In each generation, the fitness of every individual in the population is evaluated, and the fitness is usually the value of the objective function in the optimization problem being solved. However, literature [
Artificial immune systems (AIS) are concerned with abstracting the structure and function of the immune system to computational systems and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology [
Based on IGA, the association rule mining algorithm for BRM is researched in this paper. We design a multilevel association rule mining algorithm especially for BRM using IGA, which is called IGA-BRM for simplicity. There are some definitions listed below about IGA-BRM algorithm.
Antigen is the molecule, which can be identified by any antibody or
Antibody is the receptor which is produced by B lymphocytes that react with antibodies. Usually, the antibody corresponds to the optimal solution to the problem. In this paper, it refers to association rules that will be mined.
The strength of the bind between antibodies is represented by affinity. In AIS, the more similar antibodies will have the bigger values of the affinity. In order to control the density of antibodies, the new antibody is created only if the affinity between the two exceeds the affinity threshold.
The affinity between both antibodies can be calculated using (
The strength of the bind between the antigen and the antibody, which depends on how closely the two match, is called fitness. The more matching of the antibody and the antigen is, the stronger the molecular binding will be, and the better the antibody can be recognized. In IGA, the fitness indicates the adaptability of the candidate solutions of objective function to the problem.
Actually, different fitness functions are chosen according to the different problem. Therefore, fitness function is the key of convergence rate of the algorithm. Assuming the support of the antibody is
It is the proportion of the similar antibodies and the total number of antibodies in one generation. We use the symbol
With the development of the IGA, the antibodies in the populations are more and more similar, and the diversity of the antibodies is no longer maintaining the original level. In order to maintain the diversity of the antibodies, improve the global search ability, and avoid the immature convergence of the algorithm, the fitness and concentration of the generation should be controlled. The parameter called polymerization fitness is introduced to control the selected probability of antibody in the genetic process.
It is the balance of antibody concentration and the fitness of antibody, which is expressed by the following equation:
Based on the above definitions and cross level encoding scheme, steps of the IGB-BRM algorithm are described as follows.
Initialize all the parameters such as the size of population
Process the data in database including encoding the basic antibody and then converting to the antibody code using our cross level coding scheme.
Build the initial antibody population with half randomly generated and the other half taken from the coded data in Step
Calculate the values of support, confidence, and fitness of each antibody using the coded data in Step
Produce the new antibodies; execute the GA algorithm which includes selection, crossover, and mutation.
Calculate the affinity of the new antibodies, and then compare them with the corresponding thresholds
If the comparing result of Step
Produce the other new antibodies which make the information entropy and the fitness satisfy the required thresholds.
Calculate the concentration and polymerization fitness of the antibody population, and then select the
If the count number of generation
In the algorithm, the antibodies with the highest polymerization fitness will be selected as the new increasing antibodies when the antibody population is refreshed. It is the concentration based population refresh in IGA-BRM. From formula (
Resent years, researches have shown that the composition of marine traffic is seaman, ship, and environment, while the human error is the main factor which causes the accident among them. In this paper, based on vessel collision accidents on the sea, we research the deep reasons induced by human errors. The goal of our work is to reduce the maritime collision accidents caused by human error in aspect of seafarer training, assessment, and management.
Traditionally, the collision accidents of vessels are analyzed by the data from the maritime investigation reports. But the data does not reflect deep inducements which we want to guide seafarer training. For example, a collision happened which was caused by the improper lookout, when a duty officer is on duty while sailing. Maritime survey results showed that the accident is mainly due to the improper lookout which led to the vessel missing the prime time for the prevention of collision; that is, collision accident cannot be avoided despite using the avoidance measures and good seamanship when the vessel sailed into the distance to closest approach (DCPA). Usually, as the maritime competent authority, we want to make certain policy to reduce or even avoid the vessel collision accidents through analyzing and summarizing these reasons of accidents. However, in the surface of the maritime accident investigation report the only reason of the accident and the malpractice is classified as a human error accident, which lacks the analysis of the human error itself. In fact, throughout the numerous collision accidents caused by the “improper lookout,” there are many causes hiding the “improper lookout.” The author, who works in seafarers’ competency examination management and maritime investigation for several years, found that the main factors of “improper lookout” may be due to the fatigue, poor awareness of risk situation caused by overconfidence and the bad work attitude, and so on. Based on the above reasons, IMO in STCW Manila amendments proposed concept of BRM, which summarized the causes of the accident into six aspects, called items of BRM in this paper, listed in Table
Corresponding relationship between items in BRM and attribute.
Attribute | Items in BRM |
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Team work abilities and working attitude |
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Abilities of communication |
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Decision-making capacity |
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Pressure and fatigue |
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Situational awareness and ability of risk assessment |
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Educational background |
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Severity of the accident |
Corresponding relationship between human errors and basic attributes.
Basic attribute | Human error |
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Improper lookout |
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Nautical instruments use error |
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Errors in judgment |
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Inopportune navigating |
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Improper communication |
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Inappropriate maneuvering |
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Not at safe speed |
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Disobey navigation rules |
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Unable to use good seamanship |
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Error in positioning and route plan |
The data analyzed is chosen from the maritime investigation files in resent ten years in north China. There are about 1032 items. Among them, we randomly select 100 of ship collision accident investigation report and build a statistical table about the value of cross level weight, that is,
Values of
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0.2 | 0.2 | 0 | 0.1 | 0 | 0.2 | 0.7 | 0 | 0.6 | 0.4 |
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0.2 | 0 | 0.1 | 0.1 | 0.4 | 0 | 0 | 0.7 | 0 | 0 |
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0.1 | 0.2 | 0.2 | 0.2 | 0 | 0.2 | 0 | 0 | 0 | 0 |
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0.4 | 0.5 | 0.3 | 0.4 | 0.5 | 0.3 | 0.1 | 0.1 | 0.2 | 0.5 |
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0.1 | 0 | 0.4 | 0.2 | 0 | 0.3 | 0.2 | 0.2 | 0.1 | 0 |
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0 | 0.1 | 0 | 0 | 0.1 | 0 | 0 | 0 | 0.1 | 0.1 |
In the simulation, the items of BRM are considered as attributes of the algorithm, and the ten surface reasons coming from the database are as the basic attributes which have certain contributions to the attributes. Additionally, to find the relationship between items of BRM and the vessel collision accidents, the severity of the accident is added to the attributes, which can be achieved from the maritime investigation reports, denoted as
Association rules and the meaning of them based on BRM.
Association rule with support and confidence | Meaning based on BRM |
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The worst team work abilities and working attitude lead to the serious accident |
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The worst pressure and fatigue lead to the worst accident |
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The bad situational awareness and ability of risk assessment lead to the general accident |
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The worst team work abilities and attitude added to bad situational awareness and ability of risk assessment lead to the worst accident |
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The bad pressure and fatigue added to bad decision-making capacity lead to the serious accident |
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The bad abilities of communication and poor educational background lead to the general accident |
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The worst pressure and fatigue added to situational awareness and ability of risk assessment lead to the serious accident |
From the results in Table
In order to illustrate the reasonability of our method, we compare our results with the literature [
Comparing with the actual situation, the simulation results in Table Based on the Manila amendments, the item about rest time of crew needs to be built; the competent authority should control the rest time longer than the minimum requirement of STCW strictly. The Port State Control (PSC) should make the reasonable and feasible ways to protect the rest and relax right of the seaman. In terms of the vessel operators and owners, considering both the culture and character of the company, the control of the crew’s rest time and education of mental health should be incorporated in the safety management system, in order to make the crew rest enough and be happy. According to the “pressure and fatigue,” there are two ways to solve the problem. One is that IMO and the competent authority add the courses of mental health to the standard of competency ability examination for the crew, and the other is to modify the item of the minimum safety requirements for vessels, forcedly require the vessel, of which voyage exceeds a certain time, and provide the mental health counselor. The shipping company or the crew management company should authorize the master to coordinate the sailing team, in order to improve the ability of teamwork and enhance the obedience consciousness of the member. Coordinate content should focus on breakthrough teamwork’s five obstacles, named “lack of trust,” “fear of conflict,” “lack of investment,” “escape responsibility,” “ignore result.” The competent authority should evaluate the leadership, organization, and coordination capacity quantitatively and bring the ability into the master competency ability requirement. In order to enhance the crew’s ability of dealing with crisis, maritime education should focus on the situational awareness. Navigation is the dynamic process, and there are so many emergencies which demand the crew to develop the excellent response ability. Therefore, it is necessary to add the operation of navigation simulator to the maritime education and increase the investment of the study of navigation simulator for the better simulation result.
In this paper, based on immune genetic algorithm, we propose a multilevel association rule mining for BRM announced by IMO in 2010, named IGA-BRM. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents.
Because of the indirect data that can be collected which seems useless for the analysis of the relationship between items of BIM and the vessel accidents, a cross level coding scheme for mining the multilevel association rules is proposed. By this way, based on basic maritime investigation data, the relationship between items of BRM and vessel accidents is built. We execute the IGA-BRM with the cross level coding scheme for the research of BRM. As a result, based on the basic maritime investigation reports, many important association rules about the items of BRM are mined. Among these association rules, we find that the item of pressure and fatigue in BRM is the most important factor which leads to the worst or most serious accident, and team work abilities and working attitude are the second important factor that causes the serious accident, while the bad situational awareness and ability of risk assessment are the third one that causes the general accident and so on. At last of the paper, according to the pattern that mined using IGA-BRM algorithm, five suggestions are provided for the work of seafarer training, assessment, and management on the maritime competent authority level.
The authors declare that there is no conflict of interests regarding the publication of this paper.