Generalized structure of the group method of data handling for modeling iceberg drafts

IF 3.1 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Ocean Modelling Pub Date : 2024-02-13 DOI:10.1016/j.ocemod.2024.102337
Hamed Azimi , Hodjat Shiri , Masoud Mahdianpari
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Abstract

The iceberg draft prediction is vital to mitigate the collision risk of deep keel icebergs with the seafloor-founded infrastructures, including the subsea pipelines, wellheads, hydrocarbon loading equipment, and communication cables crossing the Arctic and subarctic areas since the drifting icebergs may gouge the ocean floor and the physical and operational integrity of the submarine structures would be threatened. In this study, the iceberg drafts were simulated using the generalized structure of the group method of data handling (GS-GMDH) algorithm for the first time. The parameters affecting the iceberg drafts were determined, and five GS-GMDH models comprising GS-GMDH 1 to GS-GMDH 5 were developed utilizing those parameters governing. A dataset comprising 161 distinct case studies measured in the most significant field investigations of iceberg characteristics was generated, and the GS-GMDH models were trained through 60 % of the data, the rest of the data (i.e., 40 %) were considered for the GS-GMDH models’ validation. By defining different scenarios, the most accurate GS-GMDH model and the most important input parameters were identified. The sensitivity analysis demonstrated that the iceberg width ratio (W/H) and the iceberg shape factor (Sf) were identified as the most influencing input parameters. The comparison between the performance of the premium GS-GMDH model and the group method of data handling (GMDH), artificial neural network (ANN) algorithms, and the empirical models proved that the GS-GMDH model simulated the iceberg drafts with the highest level of precision and correlation along with the lowest degree of complexity. Based on the partial derivative sensitivity analysis (PDSA), the magnitude of iceberg drafts grew by increasing the value of the iceberg width and iceberg length. Ultimately, a GS-GMDH-based equation was presented to estimate the iceberg drafts for practical applications, particularly in the early stages of iceberg management projects and engineering designs.

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冰山草案建模数据处理组法的通用结构
冰山吃水预测对于降低深龙骨冰山与海底基础设施(包括穿越北极和亚北极地区的海底管道、井口、碳氢化合物装载设备和通信电缆)的碰撞风险至关重要,因为漂移的冰山可能会刨开洋底,威胁海底结构的物理和运行完整性。在这项研究中,首次使用数据处理群法的广义结构(GS-GMDH)算法模拟了冰山吃水。确定了影响冰山吃水的参数,并利用这些参数建立了五个 GS-GMDH 模型,包括 GS-GMDH 1 至 GS-GMDH 5。生成的数据集包括在最重要的冰山特征实地调查中测量的 161 个不同的案例研究,通过 60% 的数据对 GS-GMDH 模型进行了训练,其余数据(即 40%)用于 GS-GMDH 模型的验证。通过确定不同的情景,确定了最准确的 GS-GMDH 模型和最重要的输入参数。敏感性分析表明,冰山宽度比(W/H)和冰山形状系数(Sf)是影响最大的输入参数。高级 GS-GMDH 模型与分组数据处理法(GMDH)、人工神经网络(ANN)算法和经验模型的性能比较证明,GS-GMDH 模型模拟冰山吃水的精度和相关性最高,复杂度最低。根据偏导数灵敏度分析(PDSA),冰山宽度和冰山长度的值越大,冰山吃水的幅度就越大。最后,提出了一个基于 GS-GMDH 的方程,用于估算实际应用中的冰山吃水,特别是在冰山管理项目和工程设计的早期阶段。
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来源期刊
Ocean Modelling
Ocean Modelling 地学-海洋学
CiteScore
5.50
自引率
9.40%
发文量
86
审稿时长
19.6 weeks
期刊介绍: The main objective of Ocean Modelling is to provide rapid communication between those interested in ocean modelling, whether through direct observation, or through analytical, numerical or laboratory models, and including interactions between physical and biogeochemical or biological phenomena. Because of the intimate links between ocean and atmosphere, involvement of scientists interested in influences of either medium on the other is welcome. The journal has a wide scope and includes ocean-atmosphere interaction in various forms as well as pure ocean results. In addition to primary peer-reviewed papers, the journal provides review papers, preliminary communications, and discussions.
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