OFPO & KGFPO：洪水过程观测的本体与知识图谱

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software Pub Date : 2025-01-03 DOI:10.1016/j.envsoft.2025.106317

Wenying Du, Chang Liu, Qingyun Xia, Mengtian Wen, Ying Hu, Zeqiang Chen, Lei Xu, Xiang Zhang, Berhanu Keno Terfa, Nengcheng Chen

{"title":"OFPO & KGFPO：洪水过程观测的本体与知识图谱","authors":"Wenying Du, Chang Liu, Qingyun Xia, Mengtian Wen, Ying Hu, Zeqiang Chen, Lei Xu, Xiang Zhang, Berhanu Keno Terfa, Nengcheng Chen","doi":"10.1016/j.envsoft.2025.106317","DOIUrl":null,"url":null,"abstract":"Flooding is the most frequent natural disaster globally, resulting in the highest economic losses. Efficient resource retrieval is crucial for improving flood response. Constructing a knowledge graph aids in the precise discovery of flood observation resources. However, current research faces issues: phased flood process observation is neglected, and effective correlation among disaster elements, such as tasks, data, methods, and sensors, is lacking. To address this, we construct the Ontology for Flood Process Observation (OFPO) and develop the Knowledge Graph for Flood Process Observation (KGFPO), providing integrated management and decision-making support. These are validated using the “7–20 Henan Extremely Heavy Rainfall” and “7-21 Xinxiang Extremely Heavy Rainfall” cases. OFPO and KGFPO achieve integrated management of flood observation resources, improve retrieval efficiency and accuracy, facilitate decision-making, and support other natural disasters.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"18 1","pages":""},"PeriodicalIF":4.8000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"OFPO & KGFPO: Ontology and knowledge graph for flood process observation\",\"authors\":\"Wenying Du, Chang Liu, Qingyun Xia, Mengtian Wen, Ying Hu, Zeqiang Chen, Lei Xu, Xiang Zhang, Berhanu Keno Terfa, Nengcheng Chen\",\"doi\":\"10.1016/j.envsoft.2025.106317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Flooding is the most frequent natural disaster globally, resulting in the highest economic losses. Efficient resource retrieval is crucial for improving flood response. Constructing a knowledge graph aids in the precise discovery of flood observation resources. However, current research faces issues: phased flood process observation is neglected, and effective correlation among disaster elements, such as tasks, data, methods, and sensors, is lacking. To address this, we construct the Ontology for Flood Process Observation (OFPO) and develop the Knowledge Graph for Flood Process Observation (KGFPO), providing integrated management and decision-making support. These are validated using the “7–20 Henan Extremely Heavy Rainfall” and “7-21 Xinxiang Extremely Heavy Rainfall” cases. OFPO and KGFPO achieve integrated management of flood observation resources, improve retrieval efficiency and accuracy, facilitate decision-making, and support other natural disasters.\",\"PeriodicalId\":310,\"journal\":{\"name\":\"Environmental Modelling & Software\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-01-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Modelling & Software\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1016/j.envsoft.2025.106317\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.envsoft.2025.106317","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

洪水是全球最常见的自然灾害，造成的经济损失最大。有效的资源检索是提高洪水响应能力的关键。知识图谱的构建有助于洪水观测资源的精确发现。然而，目前的研究面临的问题是：忽视了洪水过程的阶段性观测，缺乏任务、数据、方法和传感器等灾害要素之间的有效关联。为此，我们构建了洪水过程观测本体（OFPO），开发了洪水过程观测知识图谱（KGFPO），为洪水过程观测提供综合管理和决策支持。以“7-20河南特大暴雨”和“7-21新乡特大暴雨”为例进行了验证。OFPO和KGFPO实现了洪水观测资源的综合管理，提高了检索效率和准确性，便于决策，支持其他自然灾害。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

OFPO & KGFPO: Ontology and knowledge graph for flood process observation

Flooding is the most frequent natural disaster globally, resulting in the highest economic losses. Efficient resource retrieval is crucial for improving flood response. Constructing a knowledge graph aids in the precise discovery of flood observation resources. However, current research faces issues: phased flood process observation is neglected, and effective correlation among disaster elements, such as tasks, data, methods, and sensors, is lacking. To address this, we construct the Ontology for Flood Process Observation (OFPO) and develop the Knowledge Graph for Flood Process Observation (KGFPO), providing integrated management and decision-making support. These are validated using the “7–20 Henan Extremely Heavy Rainfall” and “7-21 Xinxiang Extremely Heavy Rainfall” cases. OFPO and KGFPO achieve integrated management of flood observation resources, improve retrieval efficiency and accuracy, facilitate decision-making, and support other natural disasters.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Environmental Modelling & Software 工程技术-工程：环境

CiteScore

9.30

自引率

8.20%

发文量

241

审稿时长

60 days

期刊介绍： Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.