Application of System Analysis Approach for Ocean and Coastal Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Oceans and Coastal Zones".

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 2170

Special Issue Editors


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Guest Editor
Department of Marine Environment and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan
Interests: marine environmental system analysis; system dynamics for sustainable aquatic environment; artificial intelligent and computer applications for marine

E-Mail Website
Guest Editor
Department of Marine Environment and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan
Interests: system dynamics; big data analysis; marine ecology; marine ecological simulation; marine management; sustainable development; swarm modeling

Special Issue Information

Dear Colleagues,

The ocean and coastal areas are essential components of the global life-support systems and positive assets that present opportunities for sustainable development. The Sustainable Development Goals (SDGs) adopted by the United Nations in 2015 set forth a sustainable future for all and provided a basis upon which to pursue the protection and sustainable development of ocean and coastal areas and their resources. With more than 60% of the world’s population living on the coast, the threats acting on the coastal environment are more and more interconnected. Achieving the SDGs thus requires system analysis approaches to ocean and coastal management. These approaches should be integrated in content and are either prescriptive or descriptive and deterministic or stochastic. Recent technological advances in computing and modeling should be taken advantage of in this particular direction. This Special Issue focuses on providing diverse applications of systems analyses for the benefit of ocean and coastal management. We are particularly interested in papers that discuss topics related to SDGs in ocean and coastal areas. Several system analysis approaches are listed in the keywords. Topics for submission should be related, but not strictly limited, to these keywords.

Dr. Yang-Chi Chang
Dr. Pierre-Alexandre Château
Guest Editors

Manuscript Submission Information

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Keywords

  • ocean and coastal management
  • system dynamics
  • mathematical programming
  • mathematical modeling
  • multi-criteria decision making
  • spatial analysis
  • information technology
  • artificial intelligence
  • data mining
  • machine learning

Published Papers (1 paper)

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Research

16 pages, 1323 KiB  
Article
Measurement of Coastal Marine Disaster Resilience and Key Factors with a Random Forest Model: The Perspective of China’s Global Maritime Capital
by Xiaotong Sui, Mingzhao Hu, Haoyun Wang and Lingdi Zhao
Water 2022, 14(20), 3265; https://doi.org/10.3390/w14203265 - 17 Oct 2022
Viewed by 1624
Abstract
Frequent outbreaks of marine disasters in the context of global warming pose a serious threat to the sustainable development of coastal areas and the construction of global maritime capitals. Implementing integrated marine and coastal management and assessing and enhancing cities’ resilience to marine [...] Read more.
Frequent outbreaks of marine disasters in the context of global warming pose a serious threat to the sustainable development of coastal areas and the construction of global maritime capitals. Implementing integrated marine and coastal management and assessing and enhancing cities’ resilience to marine disasters are of practical importance. Based on the capital perspective, this study innovatively constructed a framework for the Coastal Marine Disaster Resilience Index (CMDRI) for the coastal city level, considering the main marine disaster characteristics of Chinese coastal areas. Eight coastal cities in China proposed to build global maritime capitals were used as research objects. The random forest model, which can handle complex nonlinear systems and feature importance, was applied for the first time to resilience assessment and key factor identification in marine disasters. The results show that the overall level of CMDRI of each city is steadily increasing, with Shenzhen having the highest marine disaster resilience grade for each year and Zhoushan having the lowest. Economic and human capitals accounted for a more significant proportion of key factors, followed by physical and social capitals, and environmental capital accounted for a minor proportion. The comparison results of model performance show that the random forest model has better fitting accuracy and stability in assessing CMDRI and can be further applied to other disaster resilience and sustainability areas. Full article
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