## 1 Introduction  
Flood disasters are one of the 10 natural hazards [1] threatening human survival, featuring a wide influence range, strong paroxysmal, frequent occurrence, and great harm. The Human Cost of Disasters 2000-2019 report claims that global natural hazards increased significantly in the first 20 years of the 21 st century, noting that the increase in climate-related disasters has been especially \"shocking\" [2]. Flood disasters have increased from 1389 in the last 20 years to 3254, and storm disasters have increased from 1457 to 2034 [3]. The 2019 Global Natural Disaster Assessment Report showed that the annual average frequency of major global natural hazards from 1989 to 2019 was about 320, showing an initially increasing then decreasing trend. Flood and storm disasters are the most frequent, accounting for more than 60% of cases [4]. The 2020 Global Natural Disaster Assessment Report pointed out that flood disasters were the major natural hazards affecting the world in 2020 [5]. A large amount of flood disaster knowledge was buried in scientific literature. The scientific knowledge graph is a method used to quickly mine knowledge from literature for understanding global flood disaster research dynamics. It can analyze a body of literature in batches and identify research hotspots and frontiers. The scientific knowledge graph based on bibliometrics is superior to the traditional expert overview due to its simple, fast, and batch processing capabilities. [PERSON] et al. used CiteSpace to conduct descriptive statistics and bibliometric analysis of 488 studies on comprehensive disaster risks amid climate change from 2008 to 2020 [6]. [PERSON] et al. used CiteSpace and Gephi to generate national collaboration networks and discipline collaboration networks based on the literature related to natural hazards from 1900 to 2015 [7]. [PERSON] et al. used statistical methods, such as co-word analysis, cluster analysis, and multi-scale analysis based on bibliometrics to identify the research status of flash flood disasters from 1996 to 2015 [8]. [PERSON] et al. used Bibexel, VOSviewer and CiteSpace to conduct a bibliometric analysis of flood disaster risk research trends from 1990 to 2014 [9]. [PERSON] et al. used the SATI to find the main research hotspots of urban waterlogging using high-frequency keywords and journal sources from 2000 to 2015 [10]. [PERSON] et al. examined the literature on the direct economic loss due to urban rainstorms and flood disasters from 1960 to 2019 and quantitatively described the annual evolution, journal distribution, main scholars, their cooperation network characteristics, and their research focuses using CiteSpace and VOSviewer [11]. [PERSON] et al. used Citespace tools to analyze the literature on flood disaster risk in the Web of Science core database from 1999 to 2018 and identified the number of articles, distribution characteristics, and distribution of research hotspots [12]. However, the bibliometric method relying on CiteSpace analyzes only the information about the literature, such as keyword co-occurrence, institutional cooperation networks, author cooperation network and so on. In-depth mining of article content is difficult [13]. By contrast, semantic knowledge graphs can provide new research ideas. [PERSON] et al. used flood emergency tasks at various emergency stages as keywords to search for relevant Chinese studies and identified model methods from abstracts to construct a semantic knowledge graph of flood disasters [14]. [PERSON] et al. adopted various text-mining approaches, such as TF-IDF and LDA, to identify the evolution of disasters and responses through research articles [15]. [PERSON] et al. explored the disaster literature using a data mining tool to find the relationships among typhoon disasters and build a disaster network [16]. [PERSON] et al.