scholarly journals Scenario-Based Marine Oil Spill Emergency Response Using Hybrid Deep Reinforcement Learning and Case-Based Reasoning

2020 ◽  
Vol 10 (15) ◽  
pp. 5269
Author(s):  
Kui Huang ◽  
Wen Nie ◽  
Nianxue Luo
Keyword(s):  
Reinforcement Learning ◽  
Emergency Response ◽  
Oil Spill ◽  
Expert Knowledge ◽  
Decision Makers ◽  
Case Based Reasoning ◽  
Marine Oil ◽  
Knowledge Based ◽  
Challenges And Opportunities ◽  
Case Based

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.

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

scholarly journals Capitalising Experiential Knowledge for Guiding Construction Procurement Selection

2012 ◽  
Vol 5 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Thomas Ng ◽  
Chris Luu ◽  
Martin Skitmore
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Construction Project ◽  
Experiential Knowledge ◽  
Decision Makers ◽  
Management Model ◽  
Case Based Reasoning ◽  
Useful Knowledge ◽  
Procurement System ◽  
Case Based

Capitalising useful knowledge for construction procurementselection (CPS) decisions would provide a valuable asset to clientorganisations, as the successful/unsuccessful experience wouldhelp decision-makers avoid the occurrence of similar errors andensure the most suitable procurement system is employed fora construction project. As a result, there is a need to examinethe potential for developing a knowledge management model tocapture and reuse experiential knowledge to guide CPS decisions.This paper begins by identifying a suitable approach for managingCPS knowledge. This is followed by a discussion of the knowledgerequired for CPS decision support. A prototype knowledgemanagementmodel is developed, using the case-based reasoning(CBR) approach, and a mechanism for the retrieval and reuse ofknowledge for guiding CPS decisions is elucidated. The resultsindicate that CBR is a suitable tool for formulating the procurementselection parameters and selecting a suitable procurementsystem for a construction project. This is primarily becausethe CBR approach is flexible enough to allow closely matchinghistoric cases to be retrieved as well as enabling the decisionmakerto adapt the proposed solution based on the predominantcharacteristics of the client, project and external environmentpertinent to the current project.

scholarly journals Optimization on Emergency Materials Dispatching Considering the Characteristics of Integrated Emergency Response for Large-Scale Marine Oil Spills

2019 ◽  
Vol 7 (7) ◽  
pp. 214 ◽  
Author(s):  
Song Li ◽  
Manel Grifoll ◽  
Miquel Estrada ◽  
Pengjun Zheng ◽  
Hongxiang Feng
Keyword(s):  
Emergency Response ◽  
Oil Spill ◽  
Large Scale ◽  
Oil Spills ◽  
Marine Oil ◽  
Emergency Managers ◽  
Distribution Point ◽  
Dispatching Algorithm ◽  
Marine Oil Spills ◽  
The Difference

Many governments have been strengthening the construction of hardware facilities and equipment to prevent and control marine oil spills. However, in order to deal with large-scale marine oil spills more efficiently, emergency materials dispatching algorithm still needs further optimization. The present study presents a methodology for emergency materials dispatching optimization based on four steps, combined with the construction of Chinese oil spill response capacity. First, the present emergency response procedure for large-scale marine oil spills should be analyzed. Second, in accordance with different grade accidents, the demands of all kinds of emergency materials are replaced by an equivalent volume that can unify the units. Third, constraint conditions of the emergency materials dispatching optimization model should be presented, and the objective function of the model should be postulated with the purpose of minimizing the largest sailing time of all oil spill emergency disposal vessels, and the difference in sailing time among vessels that belong to the same emergency materials collection and distribution point. Finally, the present study applies a toolbox and optimization solver to optimize the emergency materials dispatching problem. A calculation example is presented, highlighting the sensibility of the results at different grades of oil spills. The present research would be helpful for emergency managers in tackling an efficient materials dispatching scheme, while considering the integrated emergency response procedure.

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