###### Abstract  
Case-based reasoning (CBR) systems often provide a basis for decision makers to make management decisions in disaster prevention and emergency response. For decades, many CBR systems have been implemented by using expert knowledge schemes to build indexes for case identification from a case library of situations and to explore the relations among cases. However, a knowledge elicitation bottleneck occurs for many knowledge-based CBR applications because expert reasoning is difficult to precisely explain. To solve these problems, this paper proposes a method using only knowledge to recognize marine oil spill cases. The proposed method combines deep reinforcement learning (DRL) with strategy selection to determine emergency responses for marine oil spill accidents by quantification of the marine oil spill scenario as the reward for the DRL agent. These accidents are described by scenarios and are considered the state inputs in the hybrid DRL/CBR framework. The challenges and opportunities of the proposed method are discussed considering different scenarios and the intentions of decision makers. This approach may be helpful in terms of developing hybrid DRL/CBR-based tools for marine oil spill emergency response. c 202010 10 5269 27 July 2020 30 July 202015 July 2020 15 July 2020 27 July 2020 30 July 2  
Many scholars have conducted in-depth research on CBR for natural disaster emergency response and support decision making. Emergency decision makers can effectively respond to emergencies with mobile-based emergency response systems such as the ontology-supported case-based reasoning (OS-CBR) system method [4]. This method has also been used to forecast the probability of finding oil slicks [5]. The generation of secondary emergencies and potential escalation into an emergency cascade has been studied with CBR and historical cases to increase the effectiveness of emergency decision making in preventing cascading disasters [6]. A CBR method based on spatiotemporal trajectory similarity assessment was applied for typhoon disasters [7]. Building information modeling (BIM) has been combined with CBR to estimate replacement costs by retrieving cost information from the industry foundation class (IFC) [8]. Various factors have contributed to the attractiveness of employing case-based methods: reducing the knowledge acquisition effort and noise tolerance based on the approximate nature of these methods and providing straightforward implementation schemes [9]. Compared with similarity matching based on subjective experience, historical knowledge and computer technology can not only accelerate the calculation process but also improve the reliability, which is of great significance in saving time during an emergency response and minimizing the damage caused by marine oil spill accidents. In particular, in the process of using CBR in disaster emergency problems, the static attribute information of the disaster case at a certain time can be employed to obtain a similarity assessment and determine which case best matches the current problem; this approach typically uses the attribute-weighted Euclidean metric for assessment [10]. However, due to the sophisticated hydrogeological and biological factors that influence oil movement, oil pollution is often characterized as an unconventional emergency and is associated with many new and unidentified risk factors, such as those related to spill magnitude uncertainties that may not be foreseeable in the disaster preparedness stage; these effects can lead to difficulty in attribute similarity assessment based on the Euclidean metric. Moreover, weight determination in CBR needs to be fully considered and requires extensive expert knowledge that may affect the timeliness of emergency decisions. Therefore, scenario analysis is of interest because it addresses variable uncertainties and has been applied in many fields, such as risk decision making in emergency response [11], and plan-making in chemical industrial park pollution accidents [12].