Extracting historical flood locations from news media data by the named entity recognition (NER) model to assess urban flood susceptibility

Fu, Shengnan; Lyu, Heng; Wang, Ze; Hao, Xinlong; Zhang, Chi

doi:10.1016/j.jhydrol.2022.128312

Cited by 8 publications

(5 citation statements)

References 53 publications

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“…Thus, the NDVI, LU and ST are used in most studies. Where part of the study area is an urban building area, the NDBI has been added as an conditioning factor in most studies on urban flood susceptibility [3]. The water system has a strong correlation with flood disasters [3,6,8,9].…”

Section: Flood Conditioning Factorsmentioning

confidence: 99%

“…Where part of the study area is an urban building area, the NDBI has been added as an conditioning factor in most studies on urban flood susceptibility [3]. The water system has a strong correlation with flood disasters [3,6,8,9]. Therefore, the DW is an important factor chosen to represent water systems in most studies.…”

Section: Flood Conditioning Factorsmentioning

confidence: 99%

“…According to Rentschler and Salhab (2020) [2], about 23% of the world's population (about 1.81 billion people) are under the threat of once-in-a-century flood. The global economic losses caused by floods reached USD 82 billion in 2021, accounting for approximately 31% of the global economic losses related to natural disasters [3]. It is certain that floods cannot be avoided.…”

Section: Introductionmentioning

confidence: 99%

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Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data

et al. 2023

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Sufficient historical flood inventory data (FID) are crucial for accurately predicting flood susceptibility using supervised machine learning models. However, historical FID are insufficient in many regions. Remote sensing provides a promising opportunity to expand the FID. However, whether the FID expanded by remote sensing can improve the accuracy of flood susceptibility modeling needs further study. In this study, a framework was proposed for improving the accuracy of flood susceptibility prediction (FSP) by combining machine learning models and the expanded FID using Sentinel-1A radar images. Five widely used machine learning models were employed to verify the accuracy of the proposed method by taking Wuhan City as a case study, including the random forest (RF), gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN) models. Sentinel-1A images from time points before, during, and after flood events were used to expand the FID for training the machine learning models. The results showed that the performance of the machine learning models for predicting flood susceptibility was improved greatly by considering the expanded FID, being improved by approximately 1.14–19.74% based on the area under the receiver operating characteristic curve (AUC). Among the used machine learning models, taking into account all the statistical indicators, the ANN showed the best performance, while the SVM showed the best generalization performance in Wuhan City. According to the results of the ANN model, approximately 19% of the area in Wuhan City, mainly distributed near rivers and lakes, is at a high flood susceptibility level. This study provides an essential reference for flood susceptibility analyses in regions with limited flood sampling data.

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Section: Flood Conditioning Factorsmentioning

confidence: 99%

Section: Flood Conditioning Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data

et al. 2023

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“…The emergence of question and answer (Q&A) systems presents an opportunity to overcome this issue. Named entity recognition (NER) is now widely used in the military ( Wang et al., 2018 ; Lu et al., 2020 ; Baigang and Yi, 2023 ; Li et al., 2023 ), entertainment and culture ( Molina-Villegas et al., 2021 ; Zhuang et al., 2021 ; Fu et al., 2022 ; Huang et al., 2022 ), cybersecurity ( Georgescu et al., 2019 ; Simran et al., 2020 ; Chen et al., 2021 ; Ma et al., 2021 ), and medicine ( Ji et al., 2019 ; Li et al., 2020 ; Wang et al., 2020 ; Liu et al., 2022 ). However, the application of NER in the agricultural sector is still in the early stages of development ( Wang et al., 2022 ; Yu et al., 2022a ; Qian et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Improved XLNet modeling for Chinese named entity recognition of edible fungus

Yu,

Wang,

Xue

2024

Front. Plant Sci.

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IntroductionThe diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem. Named entity recognition (NER) provides the basis for building an intelligent Q&A system for edible fungus. In the field of edible fungus, there is a lack of a publicly available Chinese corpus suitable for use in NER, and conventional methods struggle to capture long-distance dependencies in the NER process.MethodsThis paper describes the establishment of a Chinese corpus in the field of edible fungus and introduces an NER method for edible fungus information based on XLNet and conditional random fields (CRFs). Our approach combines an iterated dilated convolutional neural network (IDCNN) with a CRF. First, leveraging the XLNet model as the foundation, an IDCNN layer is introduced. This layer addresses the limited capacity to capture features across utterances by extending the receptive field of the convolutional kernel. The output of the IDCNN layer is input to the CRF layer, which mitigates any labeling logic errors, resulting in the globally optimal labels for the NER task relating to edible fungus.ResultsExperimental results show that the precision achieved by the proposed model reaches 0.971, with a recall of 0.986 and an F1-score of 0.979.DiscussionThe proposed model outperforms existing approaches in terms of these evaluation metrics, effectively recognizing entities related to edible fungus information and offering methodological support for the construction of knowledge graphs.

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“…The model was trained on the Microsoft Research Asia corpus, which is an available data set for the Chinese NER. The characters in the Microsoft Research Asia corpus were tagged as named entities representing persons, locations, and organizations.The model framework used in this present study is detailed inFu et al (2022).…”

mentioning

confidence: 99%

Extracting Spatiotemporal Flood Information from News Texts Using Machine Learning for a National Dataset in China

Fu,

Schultz,

Lyu

et al. 2024

Preprint

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Abstract. Urban floods present a threat in China, demanding an understanding of their spatiotemporal distribution. Current flood datasets primarily offer provincial-scale insights and lack temporal continuity, which leads to a challenge in detailed analysis. To create a consistent national dataset of flood events, this study introduces a machine learning framework by applying online news media as a primary data source to construct a county-level dataset of urban flood events from 2000 to 2022. Using the Bidirectional Encoder Representations from Transformers (BERT) model, we achieved robust performance in information extraction, with an F1 score of 0.86 and an exact match score of 0.82. Further, a combined model of Bidirectional Long Short-term Memory (BiLSTM) networks with a Conditional Random Field (CRF) layer effectively identified flood locations. Our analysis reveals that the temporal trend of flooded cities in the news-based dataset is similar to the China Flood and Drought Bulletin. Furthermore, the consistency of flood events in the news with the typhoon trajectory in two cases, and the connection between flood occurrences and flood conditioning factors, confirm the accuracy of spatial distribution. The validated news-based dataset analyzes urban floods in China from both temporal and spatial perspectives. First, this dataset shows the seasonal characteristics of flood events, which are concentrated in the summer. From 2000 to 2022, the peak year for floods was 2010, and excluding the influence of peak year, the overall temporal trend of total flood occurrence shows an increase. Spatially, the distribution of floods decreases from southeast to northwest, with Guangxi Province having the highest number of floods. Additionally, the Yangtze and Pearl River basins are most frequently affected by urban floods. The subtropical climate zone is the most susceptible to flooding. This study provides an automated and effective method for constructing a national flood event dataset and reveals the spatiotemporal characteristics of urban flooding in China.

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Extracting historical flood locations from news media data by the named entity recognition (NER) model to assess urban flood susceptibility

Cited by 8 publications

References 53 publications

Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data

Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data

Improved XLNet modeling for Chinese named entity recognition of edible fungus

Extracting Spatiotemporal Flood Information from News Texts Using Machine Learning for a National Dataset in China

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