Utilizing Geoparsing for Mapping Natural Hazards in Europe
文献类型：期刊论文
| 作者 | Yu, Tinglei1,3; Zhang, Xuezhen1,3; Yin, Jun2 |
| 刊名 | WATER
 |
| 出版日期 | 2025-12-12 |
| 卷号 | 17期号:24页码:3520 |
| 关键词 | natural hazards late Middle Ages spatial-temporal distribution literature text mining natural language processing (NLP) |
| DOI | 10.3390/w17243520 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Natural hazards exert a detrimental influence on human survival, environmental conditions and society. Historical hazard events have generated a broad corpus of literature addressing the spatiotemporal extent, dissemination or social responses. With regard to quantitative analysis based on information locked within verbose text, the release of such information from the narrative format is encouraging. Natural Language Processing (NLP), a technique demonstrated to be capable of automated data extraction, provides a useful tool in establishing a structured dataset on hazard occurrences. In our study, we utilize scattered textual records of historical natural hazard events to create a novel dataset and explore the applicability of NLP in parallel. We put forward a standard list of toponyms based on manual annotation of a compilation of disaster-related texts, all of which were references in an authoritative publication in the field. The final natural hazards dataset comprised location data, which referred to a specific hazard report in Europe during 1301-1500, together with its geocoding result, year of occurrence and detailed event(s). We evaluated the performance of four pre-trained geoparsing tools (Flair, Stanford CoreNLP, spaCy and Irchel Geoparser) for automated toponym extraction in comparion with the standard list. All four tested methods showed a high precision (above 0.99). Flair had the best overall performance (F1 score 0.89), followed by Stanford CoreNLP (F1 score 0.83) and Irchel Geoparser (F1 score 0.82), while spaCy had a poor recall (0.5). Then we divided natural hazards into six categories: extreme heat, snow and ice, wind and hails, rainstorms and floods, droughts, and earthquakes. Finally, we compared our newly digitized natural hazard dataset to a geocoded version of the dataset provided by Harvard University, thus providing a comprehensive overview of the spatial-temporal characteristics of European hazard observations. The statistical outcomes of the present investigation demonstrate the efficacy of NLP techniques in text information extraction and hazard dataset generation, offering references for collaborative and interdisciplinary efforts. |
| URL标识 | 查看原文 |
| WOS关键词 | CLIMATE VARIABILITY ; LOW-COUNTRIES ; WEATHER ; RECONSTRUCTION ; WINCHESTER ; IMPACTS ; HISTORY ; WESTERN ; RIVER ; COLD |
| WOS研究方向 | Environmental Sciences & Ecology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001646180000001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219443]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Yu, Tinglei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 2.Lhasa Tibetan Plateau Sci Res Ctr, Lhasa 850000, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Yu, Tinglei,Zhang, Xuezhen,Yin, Jun. Utilizing Geoparsing for Mapping Natural Hazards in Europe[J]. WATER,2025,17(24):3520. |
| APA | Yu, Tinglei,Zhang, Xuezhen,&Yin, Jun.(2025).Utilizing Geoparsing for Mapping Natural Hazards in Europe.WATER,17(24),3520. |
| MLA | Yu, Tinglei,et al."Utilizing Geoparsing for Mapping Natural Hazards in Europe".WATER 17.24(2025):3520. |
入库方式： OAI收割
来源：地理科学与资源研究所
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