Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
Abstract
:1. Introduction
1.1. Literature Review
1.2. Discussion of Existing Studies
- A system resilience analysis framework is explored to improve the safeguard capacity of the NSR in mitigatingvarious disruptions that affect safe transit along the NSR;
- A methodology is established to conduct a resilience assessment of the NSR based on the integration of fuzzy theory, a BN, and information entropy theory;
- The proposed fuzzy BN-based model can effectively cope with the uncertainty and unavailability of information associated with Arctic waters;
- The conduction of different types of analyses, such as forward and backward propagation, sensitivity, and information entropy analyses, helps us obtain a complete understanding of NSR resilience.
1.3. Organization
2. Proposed Resilience Measurement Methodology
2.1. Fuzzy Theory
2.1.1. Fuzzy Number Selection to Design a Questionnaire
2.1.2. Weight Determination for the Expert Capacity
- ⮚
- With respect to fuzzy numbers in pairwise comparison matrices, the geometric mean technique is applied to obtain the synthetic pairwise comparison matrix, , as follows:
- ⮚
- The fuzzy weights of the criteria for each expert can be calculated by the following equation:
- ⮚
- The fuzzy weights for each criterion are defined as follows:
- ⮚
- The weight of each expert is computed by employing the center of area technique, as follows:
2.1.3. Expert Viewpoint Aggregation
2.2. Bayesian Network Theory
2.2.1. Prior Probability Calculation for Nodes without Parents
2.2.2. Conditional Probability Table Calculation with the Noisy-OR Function
- (1)
- All the nodes in the proposed Bayesian network can be regarded as Boolean variables; that is, the nodes have binary states, true or false, representing positive or negative outcomes, respectively;
- (2)
- The causes (parent nodes) of are mutually independent;
- (3)
- The probability that is true when only one causal factor, , is true while all other factors except are false can be expressed as:
2.3. Information Entropy Theory
3. Model Established for NSR Resilience Measurement
3.1. Scenario Development
3.2. Disruption Identification
3.3. Resilience Capacity Decomposition for the Arctic Northeast Route
3.3.1. Absorptive Capacity
3.3.2. Adaptive Capacity
3.3.3. Restorative Capacity
3.4. Resilience Measurement Using the Fuzzy Bayesian Network
3.4.1. Reliability Quantification for the Employed Experts
- A heterogeneous group of experts is usually preferred to a homogenous group. In a heterogeneous group, the individual experience of each expert receives considerable attention;
- With respect to the education and experience of the experts in a field, the longer they have focused on a subject (academic or practical subject), the more accurate their intuitionistic judgement is;
- With respect to expert familiarity with a subject, especially through practical experience, an experienced specialist can theoretically master every detail of the subject.
3.4.2. Calculation of the Prior Probabilities of the Nodes without Parent Nodes
3.4.3. Calculation of the Condition Probability Table for the Network
3.5. Resilience Quantification
4. Results and Discussion
4.1. Belief Propagation Analysis
4.2. Sensitivity Analysis
4.3. Uncertainty Analysis Based on Information Entropy Theory
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Linguistic Expressions | Fuzzy Trapezoidal Numbers |
---|---|
Very Low (VL) | (0,0,0.1,0.2) |
Low (L) | (0.1,0.25,0.25,0.4) |
Medium (M) | (0.3,0.5,0.5,0.7) |
High (H) | (0.6,0.75,0.75,0.9) |
Very High (VH) | (0.8,0.9,1,1) |
Sea | March (Million km2) | September (Million km2) | Seasonal Changes |
---|---|---|---|
Barents Sea | 0.855 | 0.128 | 85% |
Kara Sea | 0.830 | 0.266 | 68% |
Laptev Sea | 0.536 | 0.196 | 63% |
East Silerian Sea | 0.770 | 0.516 | 33% |
Chukchi Sea | 0.595 | 0.196 | 67% |
No. | Disruption | Description |
---|---|---|
1 | Weather forecast inaccuracy | The weather along the Arctic Northeast Route is complex and variable, making it difficult to predict; as a result, the NSR security system suffers from information uncertainty associated with weather forecasts [60] |
2 | Sea chart incomplete | The navigation chart for the NSR is still incomplete due to complex factors such as the geological conditions, lack of hydrographic ship information, and political intervention [61] |
3 | Communication/positioning unavailable | According to the database for maritime accidents (DAMA of Det Norske Veritas), missing safety instructions and defective communication can impact the safety level of navigation in Arctic waters [62] |
4 | Malfunction of power plant | Based on the comments from experts of the Canadian Transport Agency, the malfunction of power plants, such as engine failure and power and back-up power failures, are the primary causes of ship collisions, foundering, and grounding along the NSR [11] |
5 | Damaged propeller/steering gear | The propeller and steering gears of ships that use the NSR can be severely damaged by icebergs, which cause considerable disruptions to the NSR safety [60] |
6 | Malfunction of deck machinery | In the case of extremely low temperatures, some deck machinery may malfunction, thus impacting normal ship operation along the NSR [61] |
7 | Restricted function of nav. instruments | The navigation instruments on ships may not work properly due to the influence of high latitudes, which can put the ship at risk [63] |
8 | Low temperature | Low temperatures can easily affect the performance of security-related equipment such as the hull, windlass, and mooring winch [64] |
9 | Iceberg/floating ice | The existence of sea ice, such as icebergs, floating ice, and old ice, is the main feature that distinguishes the NSR from other sea lanes globally; the impact of ice on the safety of ships is continuous and inevitable [65] |
10 | Poor visibility | Poor visibility caused by steam fog, ice fog, blowing snow, and other processes along the NSR is frequently encountered and limits the watchkeeping of navigation officers [64] |
11 | Rough sea | Most of the currents in the NSR are along the coast of a shallow-water continental shelf, and the currents in the narrow straits between the islands are strong, thus creating challenges for polar navigation [66] |
12 | Magnetic storm | Magnetic storms can greatly disturb the NSR security system in the field of communication and influence navigation instruments [35] |
13 | Obstacles other than ice | Underwater obstacles such as reefs, beaches, and unknown explosives are potential disruptions that threaten the effectiveness of the NSR precautions [67] |
14 | Seafarer competency | Seafarers on ships sailing along the NSR are faced with considerable ship handling and emergency challenges, which can potentially affect the function of the NSR precautions [61] |
15 | Geopolitics | The ships sailing along the NSR have to consider different laws and regulations, including those at the local, national, and international level; additionally, sometimes political considerations are involved, making the decision-making process very complex. [68] |
Features | Description |
---|---|
Aid-to-navigation facility | The aid-to-navigation (A-to-N) facilities, including visual and audible A-to-N facilities, racons, radar marks, and shore-based Automatic Identification System (AIS) stations, are critical for navigating safely along the NSR. There are approximately 1240 coastal visual signs and 300 floating markers associated with the NSR [68]. |
Skilled seafarer team | Compared with conventional shipping route, the NSR is characterized by lots of special risks. The skilled seafarer team can swiftly evaluate the situation and take effective countermeasures with teamwork to deal with potential risks. In addition, a skilled seafarer team can develop a harmonious atmosphere in which everyone can limit the defense risk. |
Critical facility redundancy | The redundant critical facilities, such as those that provide emergency power backup, high power conservation, extra positioning techniques, and backup navigation instruments, are able to strengthen the robustness of the security system and restrict the consequences of disruptions. |
Equipment for arctic environment | The equipment designed for the arctic environment can effectively defend against or absorb the risks caused by weather condition, such as low temperatures, frost, and moisture, especially the equipment allocated on deck, including cranes, mooring winches, and windlasses, which are exposed to the external environment. |
Coordination with icebreaker | Icebreaker assistance operations play an essential role in ice-covered waters to reduce the risk of accidents, such as ice collisions and propeller or rudder damage. In addition, the case of trapped vessels by ice can also be avoided, and the disruption introduced by large amounts of floating ice can be absorbed [8]. |
Ice pilots/navigators | The assistance provided by ice pilots or navigators can defend against or attenuate various risks caused by disruptions; this approach is suitable for the case in which a shipmaster has little experience navigating in ice along the NSR. The organizations providing ice pilot services can be obtained from the NSRA [68]. |
Skilful emergency response | Under unpredictable situations related to heavy fog, floating ice, and strong wind in the NSR region, a proficient emergency response group can establish counteractive measures to response to the threats. Besides, a skilled seafarer can fully utilize the available resources, which are essential for mitigating various risks [27]. |
Response plan | A detailed response plan corresponding to various predictable disruptions and risks encountered along the NSR is useful for guiding seafarers or operators to take appropriate actions during disruptions, thus keeping the disruption or risk controllable. |
Navigational publications | Navigational publications refer to charts and other navigational publications, such as those used for guidance in arctic navigation, lists of radio signals, and notices to mariners; ice charts, which are useful for monitoring the risk related to sea ice, are particularly important [67,70] |
Features | Description |
---|---|
Ice-breaking capacity | The ice-breaking capacity allows the vessels to adapt to a risk or disruption caused by floating ice in ice-covered waters along the NSR. The ice-breaking capacity can be improved by strengthening the hull to bear ice loads for safe navigation in ice fields [70]. |
Information prediction services | Information prediction services include those for sea ice coverage, thickness, and motion, as well as weather conditions [1]. Under the conditions of predictable information, in the case of a disruption, seafarers and operators can take action in advance, for example, by anchoring to avoid a disruption or risk, changing the designed route, or requesting assistance from an icebreaker. |
Preparedness for arctic shipping | Before navigating into ice-covered waters, vessels must be fully prepared, including obtaining extra fuel reserves, psychological preparation, understanding all the regulations that must be observed, preparing for low temperatures, etc.; in case of a disruption, the vessel can make some changes based on their preparations to adapt to the new situation caused by the disruption [70]. |
Arctic communication | Arctic communication, including radar, radio, and International Maritime Satellite Organization (INMARSAT) communication, can coordinate operations from ship to ship and ship to icebreaker to adapt to the harsh environment and sailing conditions along the NSR [67]; additionally, Arctic communication helps vessels in the NSR region get assistance and guidance during disruptions, thus making risks controllable. |
Features | Description |
---|---|
Rescue and anti-pollution facility | Rescue and anti-pollution facilities are utilized to restore the shipping capacity of the NSR after natural disasters and accidents. Currently, there are three rescue and research centers established temporally along the NSR from July to October. In addition, the rescue and anti-pollution capacities of the ports (20 approximately) along the NSR need to be improved for the quick restoration of the damaged route. |
Ship repair facility | Ship repair facilities, such as yards, docks, gate operations, cranes, and warehouses, are essential for vessels that experience hull damage, machinery malfunctions, and propeller damage. Notably, the restorative capacity aimed at damaged ship repair will be limited if the stakeholders have little interest in investment. |
Human-based resources | The restorative capacity associated with human-based resources includes service restoration and technology restoration, which are substantial parts of the post-disaster strategy. Restoration may include communication, navigation, pilot service, ice-breaker assistance, and weather and hydrology information systems. |
Item | Age | Occupation | Educational Level | Certificate Rank | Job Tenure |
---|---|---|---|---|---|
Expert 1 (E1) | 53 | Senior seafarer | Bachelors of navigation | Senior Captain | He has been working on board a ship for nearly 25 years; as a senior captain, he sailed the Arctic Northeast Route recently. |
Expert 2 (E2) | 50 | Senior seafarer | Bachelors of navigation | 2nd Officer | He has been working on board a ship for nearly 15 years; currently, he is certified as a Chief Officer working on a ship capable of navigating the NSR. |
Expert 3 (E3) | 48 | Professor | Ph.D. of navigation | Senior Captain | He has been working on board ships since 1991 and obtained the certificate of senior captain; currently, he is an associate professor focusing on Arctic sea transport. |
Expert 4 (E4) | 41 | Associate professor | Masters of marine engineering | Chief engineer | He has been working on ships since 2001, beginning as a cadet and eventually becoming a chief engineer; currently, he is an associate professor focusing on risk assessments of Arctic transport. |
Expert 5 (E5) | 43 | Safety manager | Masters of navigation technology | Chief officer | He has been working on board ships since 2001, eventually becoming a chief officer; he is familiar with marine operation and equipment management for ships transiting Arctic waters. |
Indicator | Classification | Score | Indicator | Classification | Score |
---|---|---|---|---|---|
Professional position | Senior academic | 5 | Experience (2) | 6–9 | 2 |
Junior academic | 4 | ≤5 | 1 | ||
Engineer | 3 | Education level | Ph.D. | 5 | |
Technician | 2 | Masters | 4 | ||
Worker | 1 | B.S. or B.E. | 3 | ||
Age | ≥50 | 4 | Junior college | 2 | |
40–49 | 3 | School level | 1 | ||
30–39 | 2 | Certificate rank | Senior Cap. or C/E | 5 | |
≤30 | 1 | Cap. or C/E | 4 | ||
Experience (1) | ≥30 years | 5 | C/O or 2/E | 3 | |
20–29 | 4 | Operational Officer/engineer | 2 | ||
10–19 | 3 | ratings | 1 |
Expert | Position | Experience | Education | Age | Certificate | Weight |
---|---|---|---|---|---|---|
Expert 1 | Senior seafarer | 30 | Bachelors | 53 | Senior Captain | 0.189 |
Expert 2 | Senior seafarer | 27 | Bachelors | 50 | 2nd Officer | 0.121 |
Expert 3 | Professor | 18 | Ph.D. | 48 | Senior Captain | 0.300 |
Expert 4 | Associate professor | 22 | Masters | 41 | Chief engineer | 0.206 |
Expert 5 | Safety manager | 8 | Masters | 43 | Chief officer | 0.184 |
Item | Aggregated Value | Item | Aggregated Value | Item | Aggregated Value |
---|---|---|---|---|---|
D1-1 | 0.6528 | D3-4 | 0.5325 | R1-6 | 0.67 |
D1-2 | 0.4615 | D3-5 | 0.5445 | R1-7 | 0.6198 |
D1-3 | 0.7225 | D4-1 | 0.4355 | R2-1 | 0.5445 |
D2-1 | 0.7084 | D4-2 | 0.7807 | R2-2 | 0.7085 |
D2-2 | 0.6036 | D4-3 | 0.6438 | R2-3 | 0.5021 |
D2-3 | 0.7338 | R1-1 | 0.5887 | R2-4 | 0.6635 |
D2-4 | 0.7138 | R1-2 | 0.8142 | R2-5 | 0.8594 |
D3-1 | 0.6468 | R1-3 | 0.4994 | R3-1 | 0.7897 |
D3-2 | 0.7818 | R1-4 | 0.5079 | R3-2 | 0.6083 |
D3-3 | 0.6641 | R1-5 | 0.7892 | R3-3 | 0.6097 |
Expert Judgement | |||||
---|---|---|---|---|---|
: | M | H | H | M | H |
: | M | L | L | M | VH |
: | VH | H | M | H | H |
Aggregation of expert judgement (the same method as adopted for the aggregated value in Table 10) | |||||
Conditional probability calculation by the Noisy-OR model | |||||
…… | |||||
Conditional probability table for the node “nav. Service out of order” (Y) | |||||
State of the Absorptive Capacity | True | False |
---|---|---|
Expression | PDO × ASC | 0 |
State of the Post-Disruption Capacity | True | False |
---|---|---|
Expression | LC × 0.80 | 0 |
Scenario | R1-2 | R2-2 | R3-1 | Absorptive Capacity (%) | Adaptive Capacity (%) | Restorative Capacity (%) | Resilience (%) |
---|---|---|---|---|---|---|---|
Base | -- | -- | -- | 93.4 | 98.0 | 88.2 | 78.5 |
1 | false | -- | -- | 89.2 (↓) | 98.0 | 88.2 | 78.1 (↓) |
2 | false | false | -- | 89.2 | 94.8 (↓) | 88.2 | 77.8 (↓) |
3 | false | false | false | 89.2 | 94.8 | 65.7 (↓) | 75.9 (↓) |
Capacity Type | Conditional Entropy | Mutual Information | Implication of Mutual Information |
---|---|---|---|
Absorptive capacity () | 0.421 | 0.322 | The uncertainty of system resilience can be reduced by 32.2% in the case of a good knowledge of absorptive capacity |
Adaptive capacity () | 0.440 | 0.303 | The uncertainty of system resilience can be reduced by 30.3% in the case of a good knowledge of adaptive capacity |
Restorative capacity () | 0.542 | 0.201 | The uncertainty of system resilience can be reduced by 20.1% in the case of a good knowledge of restorative capacity |
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Qiao, W.; Ma, X.; Liu, Y.; Lan, H. Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network. Appl. Sci. 2021, 11, 3619. https://doi.org/10.3390/app11083619
Qiao W, Ma X, Liu Y, Lan H. Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network. Applied Sciences. 2021; 11(8):3619. https://doi.org/10.3390/app11083619
Chicago/Turabian StyleQiao, Weiliang, Xiaoxue Ma, Yang Liu, and He Lan. 2021. "Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network" Applied Sciences 11, no. 8: 3619. https://doi.org/10.3390/app11083619
APA StyleQiao, W., Ma, X., Liu, Y., & Lan, H. (2021). Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network. Applied Sciences, 11(8), 3619. https://doi.org/10.3390/app11083619