Intelligent geospatial maritime risk analytics using the Discrete Global Grid System

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2021-09-13 DOI:10.1080/20964471.2021.1965370
A. Rawson, Z. Sabeur, M. Brito
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引用次数: 12

Abstract

ABSTRACT Each year, accidents involving ships result in significant loss of life, environmental pollution and economic losses. The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence. Yet, such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures. This paper proposes the use of the Discrete Global Grid System (DGGS) as an efficient and advantageous structure to integrate vessel traffic, metocean, bathymetric, infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings. Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach. A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002. The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures, targeted to regions with the highest risk. Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.
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使用离散全球网格系统的智能地理空间海上风险分析
每年,船舶事故都会造成重大的人员伤亡、环境污染和经济损失。通过减少风险来促进航行安全需要评估相对发生可能性的空间分布的方法。然而,这种方法需要集成大量异构数据集,而这些数据集不适合传统的数据结构。本文提出利用离散全球网格系统(Discrete Global Grid System, DGGS)作为一种高效且具有优势的结构,整合船舶交通、海洋气象、水深、基础设施和其他相关海事数据集,预测船舶搁浅的发生。大规模和异构数据集非常适合机器学习算法,本文利用这种方法开发了基于DGGS的空间海上风险模型。随机森林算法用于预测接地频率和空间分布,R2为0.55,均方误差为0.002。由此产生的风险图有助于决策者规划针对风险最高的区域分配缓解措施。通过将DGGS建立为全球海洋空间数据结构,确定了进一步的工作,以扩大应用和见解。
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来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
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