# Automatic Extraction of Flooding Control Knowledge from Rich Literature Texts Using Deep Learning
## 1 Introduction  
With global warming, the frequency and intensity of hydrometeorological disasters have increased, and harm to human society and the ecological environment is becoming increasingly serious. Existing flood disaster research has accumulated rich knowledge resources that focus on risk assessment, early warning, flood frequency, disaster management, impact factor analysis, and other related topics [1]. Data resources for flood disaster risk reduction mainly include monitoring and reporting, real-time remote sensing, historical disasters, socio-economics, and basic geography [2]. Academic literature, as a kind of disaster risk and reduction data containing rich knowledge, should also be effectively utilized. Academic literature is a type of high-quality text data in a standardized format that contains peer-reviewed knowledge, condenses the achievements of scientific researchers,and is a professional resource with high research value [3]. It can be effectively utilized as a type of disaster risk and reduction data source containing rich knowledge. This knowledge in the manuscripts generally contain research methods, data sources, temporal and spatial attributes of disaster events, disaster losses, and other disaster information of importance. Since literature studies can be repeated and scaled up by other scholars anywhere in the world, they are important long-tail data resources for disaster research. Therefore, the extraction of knowledge related to flood control from these documents is important and meaningful. However, there is a certain challenge in knowledge extraction based on massive literature. In the digital era, the number of academic papers published has increased dramatically. The number of scientific and technological paper produced worldwide has reached one million and continues to increase at a rate of approximately 3% annually [4]. It is impossible to manually read all of the papers in a certain field to grasp their research status. Academic literature is an example of a classic, unstructured, long-text data. It is time-consuming, laborious, and would ultimately be an incomplete endeavor to master flood control knowledge in academic literature through manual reading. Hence, it is necessary to use intelligent methods for knowledge extraction from a large number of literature documents. In recent years, artificial intelligence methods based on big data for text mining have been favored by many scholars. The literature is composed of sentences. Therefore, it is appropriate to conduct knowledge extraction research and semantic classification at the sentence level. The current sentence classification methods are divided into three main categories: rule-based methods [5; 6; 7], machine learning, and artificial neural network-based deep learning methods. Machine learning algorithms build a model based on sample data, known as training data, to make predictions or decisions without explicit programming to do so. Deep learning is a class of machine learning algorithms based on artificial neural networks that have multiple intermediate layers. Its basic principle is to extract low-level features first, and then arrange and combine low-level features at a higher level to find useful information after combination. The research progress, advantages, and disadvantages of these methods are compared in Table 1.  
In general, rule-based methods solve problems intuitively, but it takes more time to design rules, with limited coverage, which is not suitable for massive amounts of literature. Machine learning methods incur high costs and considerable time for feature engineering. It also ignores contextual information, making it difficult to learn the semantic information