{"title":"冰山草案建模数据处理组法的通用结构","authors":"Hamed Azimi , Hodjat Shiri , Masoud Mahdianpari","doi":"10.1016/j.ocemod.2024.102337","DOIUrl":null,"url":null,"abstract":"<div><p>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 (<em>W</em>/<em>H</em>) and the iceberg shape factor (<em>S<sub>f</sub></em>) 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.</p></div>","PeriodicalId":19457,"journal":{"name":"Ocean Modelling","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generalized structure of the group method of data handling for modeling iceberg drafts\",\"authors\":\"Hamed Azimi , Hodjat Shiri , Masoud Mahdianpari\",\"doi\":\"10.1016/j.ocemod.2024.102337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 (<em>W</em>/<em>H</em>) and the iceberg shape factor (<em>S<sub>f</sub></em>) 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.</p></div>\",\"PeriodicalId\":19457,\"journal\":{\"name\":\"Ocean Modelling\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-02-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ocean Modelling\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1463500324000246\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ocean Modelling","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1463500324000246","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Generalized structure of the group method of data handling for modeling iceberg drafts
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.
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.