## 2 Materials and Methods
### Research Methods and Data Processing  
Figure 1 shows the technical roadmap of the study. The study first performed literature data acquisition via web crawlers from DRRKS. Then, a logistic model was trained to classify the literature themes. On this basis, literature screening was conducted to obtain the main data of this study. For the screened abstracts, dictionary matching was used to analyze flood disaster subcategories, and Stanford named entity recognition (NER) was used to identify flood disaster research hot areas. For the screened research institutions, Spacy NER was used to analyze the distribution law of the authors' research institutions. Finally, the crawled and extracted disaster information was integrated to construct a flood disaster knowledge graph.  
(1). Literature theme classification  
This study divided the literature into 10 themes based on the emergency tasks listed in China's emergency plans and procedures for natural hazards relief: flood risk, flood monitoring, flood disaster prediction and early warning, flood mapping, flood frequency, influencing analysis, flood disaster management, flood related research, other research with flood as the background, and completely unrelated research. Approximately 20% of the more than 14,000 flood-related studies were randomly selected as sample data for manual labeling. Then, this group was divided into training and verification according to a ratio of 7:3 to train and verify the logistic classification model. Finally, a trained logistic regression model was used to classify the other data in batches.  
(2). Analysis of flood disaster subcategories  
The Hazard Definition & Classification Review released by the Integrated Research on Disaster Risk (IRDR) in 2020 divides flood disasters into 10 categories: coastal, estuarine, flash, fluvial, groundwater water, ice jam, ponding, snowmelt, surface water, and glacial lake outburst flood. On this basis, urban, storm surges, and rainfall were added to the list. Therefore, this study divided flood disasters into 13 subcategories. Based on the results of the literature theme classification, the flood disaster literature items were rescreened, and the \"other research with flood as background\" and \"completely unrelated research\" categories were removed. The abstracts of the 5181 selected articles were used as the corpus, and the dictionary matching method was used to identify the flood disaster literature subclass.  
(3). Identification of flood disaster hotspot research areas  
The hot research areas on flood disasters were identified from the abstracts using the Stanford NER tool and location analysis. The study used English word segmentation with the Stanford tokenizer, stop word filtering, part of speech tagging with the Stanford Postagger, and English NER with the Stanford NER Tagger. Then, the location entities were screened as disaster research areas to conduct location analysis using Xpath and the Geonames placename dictionary to identify the country corresponding to each location. Finally, statistical analyses were performed.  
(4). Analysis of research institutions  
The literature offers important information about the author's institutions, including the organization name, city, zip code, and country. Therefore, this study used the Spacy NER to identify ORG (such as companies, organizations, or institutions) and GPE (such as country, city, or state) entities from the institutional information. The study used Geonames as a placename dictionary to match the GPE entity to identify the country to which the GPE belonged and employed crawls using Xpath. Finally, ArcGIS10.2 was used for map visualization to explore the spatial distribution characteristics of research institutions in the field of flood disasters.