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.

A systematic review of natural language processing applications for hydrometeorological hazards assessment

“…Critical infrastructure is mentioned in just a tiny percentage of the tweets, indicating little infrastructure-related information on social media However, these learning-based systems are difficult to adapt to new catastrophes because of the semantic changes of social media posts over different disasters, which takes a lot of effort to label a new training data set Less research has been done on the use of social media for systematic sensing of infrastructure conditions (such as functioning, damage, and restoration) Zhang et al ( 2021 ) Using economic and social spatial semantics, the new GeoSemantic2vec algorithm can learn the spatial context linkages from POI data to generate urban functional zones (the regions assigned to various social and economic activities) In the urban environment, such data records human behavioral actions, such as POI, taxi tracks, Twitter, Flickr images, etc. based on a data-driven analysis of the socioeconomic danger of floods to humans and crowdsourcing data extraction of flood areas that also takes into account the socioeconomic characteristics of flood locations It does not weight data based on POI's floor area The urban functional zones labeled as Other need to be improved Because of the arithmetic power constraint, we set a low spatial resolution and only acquired a small amount of semantic data New supervised classification techniques are pending since the remote sensing picture classification requirements do not entirely match Not ready for operational use Zhang and Wang ( 2022 ) A model was used to categorize many articles and discover essential entities quickly Global scale data (including many studies from all over the world) Rainfall, coastal flooding, and flash floods, frequent hazard types, are the main topics of study on flood disasters Full-text literature mining; data extraction from images and tables; multilingual literature The primary data utilized in this research includes information from disaster news reports, disaster site data, and essential geographical data Recognize the influencing elements and consider them in evaluations and simulation prediction models to ensure the outcomes represent reality as closely as possible The geographical disparity in the spatial distribution of flood research at the global and intercontinental scales Reynard and Shirgaokar ( 2019 ) Good classifier (sentiment analysis) Twitter is a valuable tool for social scientists since it allows for the fast and low-cost collection of massive amounts of data Twitter data may be used to create policy before, during, and after crises Location information included in tweets may be helpful to learn more about content important to policy This method inc...…”

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