Global Flood Disaster Research Graph Analysis Based on Literature Mining

Abstract

Floods are the most frequent and highest-impact among the natural disasters caused by global climate change. A large number of flood disaster knowledge were buried in the scientific literature. This study mines research trends and hotspots on flood disasters and identifies their quantitative and spatial distribution features using natural language process technology. The abstracts of 14,076 studies related to flood disasters from 1990 to 2020 were used for text mining. The study used logistic regression to classify themes, adopted the dictionary matching method to analyze flood disaster subcategories, analyzed the spatial distribution characteristics of research institutions, and used Stanford named entity recognition to identify hot research areas. Finally, the disaster information was integrated and visualized as a knowledge graph. The main findings are as follows. (1) The research hotspots are concentrated on flood disaster risks and prediction. Rainfall, coastal floods, and flash floods are the most-studied flood disaster sub-categories. (2) There are some connections and differences between the physical occurrence and research frequency of flood disasters. Occurrence frequency and research frequency of flood disasters are correlated. However, the spatial distribution at the global and intercontinental scales is geographically imbalanced. (3) The study’s flood disaster knowledge graph contains 39,679 nodes and 64,908 edges, reflecting the literature distribution and field information on the research themes. Future research will extract more disaster information from the full texts of the studies to enrich the flood disaster knowledge graph and obtain more knowledge on flood disaster risk and reduction.