Application of Social Sensors in Natural Disasters Emergency Management: A Review

Shi, Kaize; Peng, Xueping; Lü, Hao; Zhu, Yifan; Niu, Zhendong

doi:10.1109/tcss.2022.3211552

Cited by 17 publications

(3 citation statements)

References 151 publications

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“…Since the beginning of the 21st century, the frequency and quantity of major global disaster events have been increasing due to climate change [1]. Natural disasters such as heavy rainfall and floods, droughts, landslides, and earthquakes have become more frequent, widespread, and intense, posing significant challenges to emergency decisionmaking, response, and assessment.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

Wang,

Shi,

Yang

et al. 2024

IJGI

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The development of information technology has led to massive, multidimensional, and heterogeneously sourced disaster data. However, there’s currently no universal metadata standard for managing natural disasters. Common pre-training models for information extraction requiring extensive training data show somewhat limited effectiveness, with limited annotated resources. This study establishes a unified natural disaster metadata standard, utilizes self-trained universal information extraction (UIE) models and Python libraries to extract metadata stored in both structured and unstructured forms, and analyzes the results using the Word2vec-Kmeans cluster algorithm. The results show that (1) the self-trained UIE model, with a learning rate of 3 × 10−4 and a batch_size of 32, significantly improves extraction results for various natural disasters by over 50%. Our optimized UIE model outperforms many other extraction methods in terms of precision, recall, and F1 scores. (2) The quality assessments of consistency, completeness, and accuracy for ten tables all exceed 0.80, with variances between the three dimensions being 0.04, 0.03, and 0.05. The overall evaluation of data items of tables also exceeds 0.80, consistent with the results at the table level. The metadata model framework constructed in this study demonstrates high-quality stability. (3) Taking the flood dataset as an example, clustering reveals five main themes with high similarity within clusters, and the differences between clusters are deemed significant relative to the differences within clusters at a significance level of 0.01. Overall, this experiment supports effective sharing of disaster data resources and enhances natural disaster emergency response efficiency.

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Section: Introductionmentioning

confidence: 99%

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

Wang,

Shi,

Yang

et al. 2024

IJGI

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“…With the advancement of water conservancy informatization, the field of emergency management for water diversion project has accumulated a vast array of document-based emergency plans. These plans exhibit characteristics such as significant scale, diverse structures, and an abundance of professional terminology [ 2 , 3 ]. Currently, emergency decision-makers are still required to manually peruse and search for document-based emergency plans when formulating emergency strategies for unforeseen events.…”

Section: Introductionmentioning

confidence: 99%

Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network

Wang,

Liu,

Liu

et al. 2023

PLoS ONE

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Using information technology to extract emergency decision-making knowledge from emergency plan documents is an essential means to enhance the efficiency and capacity of emergency management. To address the problems of numerous terminologies and complex relationships faced by emergency knowledge extraction of water diversion project, a multi-feature graph convolutional network (PTM-MFGCN) based on pre-trained model is proposed. Initially, through the utilization of random masking of domain-specific terminologies during pre-training, the model’s comprehension of the meaning and application of such terminologies within specific fields is enhanced, thereby augmenting the network’s proficiency in extracting professional terminologies. Furthermore, by introducing a multi-feature adjacency matrix to capture a broader range of neighboring node information, thereby enhancing the network’s ability to handle complex relationships. Lastly, we utilize the PTM-MFGCN to achieve the extraction of emergency entity relationships in water diversion project, thus constructing a knowledge graph for water diversion emergency management. The experimental results demonstrate that PTM-MFGCN exhibits improvements of 2.84% in accuracy, 4.87% in recall, and 5.18% in F1 score, compared to the baseline model. Relevant studies can effectively enhance the efficiency and capability of emergency management, mitigating the impact of unforeseen events on engineering safety.

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“…With the rapid development of the information age, the amount of data generated by the Internet has increased dramatically [1][2][3]. When users are faced with these massive amounts of data, it is difficult to quickly obtain useful information.…”

Section: Introductionmentioning

confidence: 99%

A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition

Mao,

Cheng,

Shi

2023

Applied Sciences

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In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interested in. However, traditional recommendation methods only rely on user rating data for jobs and provide recommendation services for recruiters and candidates through simple information matching. This simple recommendation strategy not only causes a lot of information waste but also cannot effectively utilize the multi-source heterogeneous data information in the field of job recruitment. Therefore, this paper proposes a job recommendation model based on users’ attention levels and tensor decomposition for specific recruitment positions. This model puts forward assumptions based on browsing time for the special behaviors and habits of users in the field of job recruitment, defines corresponding label values for different interactive behaviors, and establishes a grading method based on the attention of job seekers, thus constructing a three-dimensional tensor of “job seeker user-position-attention layered”. Then, a recommendation model is constructed by decomposing the three-dimensional tensor. The effectiveness of the model is verified by comparative experiments with other recommendation algorithms.

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Application of Social Sensors in Natural Disasters Emergency Management: A Review

Cited by 17 publications

References 151 publications

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network

A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition

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