# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 1 Introduction
### Literature Review  
The risk management of arctic shipping operations seeks to minimize the risk level. One of the most important issues is to identify the causal factors that contribute to risk or accidents. Therefore, many studies have been conducted to investigate the risk factors based on accidents and the unique natural environment in Arctic waters; such methods involve applying various theories, models, and techniques. Fault tree analysis was utilized by [7] to investigate the risk factors involved in chemical tanker accidents in Arctic waters. Later, an integrated methodology including fault tree analysis and the Human Factor Analysis and Classification System (HFACS) model was developed by [8] to study the collision risk during icebreaker assistance. Records of navigational shipping accidents that occurred in the Northern Baltic Sea during 2007 and 2013 were collected and investigated by [9], who found that ridged ice of a certain thickness cannot be considered the main cause of accident occurrence. Ref. [10] investigated the risk factors associated with Arctic maritime accidents from 1993 to 2011 by performing root cause analysis. Ref. [10] summarized various navigational risk contributors when sailing along an Arctic route, and the corresponding numerical weights were also given by using fuzzy analytic hierarchy process (AHP) and expert elicitation. The risk factors can also be identified by the application of Bayesian networks. Later, a novel Bayesian network (BN) model was proposed by [11] to identify the shipping operation risk in Arctic waters. Some studies based on the perspective of navigators have been performed to evaluate the shipping operation risk in Arctic waters, and such studies include those of [12; 13], who studied the navigation risk in icy waters in the Baltic Sea. Ref. [6] investigated the key factors that influence the operation of arctic shipping routes. Resilience is widely regarded as a specified capacity factor pertaining to an organization or a system that reflects how well the organization or system can recover from a disrupted or destroyed state. The concept of resilience was first introduced by [14], who argued that resilience can enable an ecosystem to continue to operate after being disturbed. Later, many studies tried to define resilience [15; 16; 17]; however, there is currently no universal consensus on the definition of resilience. Based on its various definitions, resilience is widely used in different fields [18; 19; 20; 21] due to its contribution to the development of the relationship between capacity and a disruption or disaster. Many important applications of system resilience are subject to infrastructure systems, such as urban infrastructure systems [22], electrical infrastructure systems [23], inland waterway ports [24], and full-service deep-water ports [25]. In addition, organizational resilience was defined by [17] as the ability of enterprises and other organizations to improve performance. Similarly, in the social domain, the resilience capacity is regarded as \"the ability of communities to cope with external stresses and disturbances as a result of social, political, and environmental change\" [26]. In past decades, substantial differences existed among various descriptions of system resilience; however, the quantification of system resilience has always been a common concern. Generally, system resilience can be regarded as a time-dependent variable, as shown in Figure 1. Initially, the system is stable in the original state; then, with the appearance of a disruptive event, the performance of the system deteriorates gradually, which can trigger system resilience to recover the system performance to the normal level. According to [27], system resilience can be decomposed into absorptive capacity, adaptive capacity, and restorative capacity, which can absorb the shocks causedby a disruption, adapt the system to the new disrupted conditions, and restore the system to normal conditions as much as possible, respectively.