Knowledge Graph Representation Learning-Based Forest Fire Prediction

Chen, Jiahui; Yang, Y.; Peng, Ling; Chen, Luanjie; Ge, Xingtong

doi:10.3390/rs14174391

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Qu [26] synthesized the structure and construction techniques of medical knowledge graphs according to the characteristics of big data in the medical field, such as strong specialization and complex structure, and reviewed the application of knowledge graph technology in the medical field. Chen et al [27] proposed a forest fire prediction method based on knowledge graphs and representation learning. Huang et al [28] constructed methods and proposed applications of knowledge graphs in oil exploration and development.…”

Section: Knowledge-based Question Answeringmentioning

confidence: 99%

Civil Aviation Travel Question and Answer Method Using Knowledge Graphs and Deep Learning

Gong,

Guan,

Sun

et al. 2023

Electronics

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In this paper, a civil aviation travel question and answer (Q&A) method based on integrating knowledge graphs and deep learning technology is proposed to establish a highly efficient travel information Q&A platform and quickly and accurately obtain question information and give corresponding answers to passengers. In the proposed method, a rule-based approach is employed to extract triads from the acquired civil aviation travel dataset to construct a civil aviation travel knowledge graph. Then, the ELECTRA-BiLSTM-CRF model is constructed to recognize the entity, and an improved ALBERT-TextCNN model is used for intent classification. Finally, Cypher query templates are transformed into Cypher query statements and retrieved in the Neo4j database, and the query returns the result, which realizes a new civil aviation travel Q&A method. A self-built civil aviation dataset is selected to prove the effectiveness of the proposed method. The experimental results show that the proposed method based on integrating knowledge graphs and deep learning technology can achieve better Q&A results, and it has better generalization and high accuracy.

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Section: Knowledge-based Question Answeringmentioning

confidence: 99%

Civil Aviation Travel Question and Answer Method Using Knowledge Graphs and Deep Learning

Gong,

Guan,

Sun

et al. 2023

Electronics

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“…The baseline models selected for this study are widely used named entity recognition models and relation extraction models. The baseline models include BILSTM [21], FastText [23], TextCNN [27], BERT-CNN [41], and BERT-BILSTM-CRF [43,44]. Table 2 presents the parameter configurations for each model.…”

Section: Data Set and Baseline Modelmentioning

confidence: 99%

“…With the emergence of advanced pre-training models such as bidirectional encoder representation from transformers (BERT) [35], generative pre-trained transformer (GPT) [36], and text-to-text transfer transformer (T5) [37], researchers have endeavored to leverage these models to compensate for the limitations of CNN [38][39][40]. In the literature [41], the amalgamation of BERT and CNN models gives birth to an innovative methodology for extracting knowledge related to fire emergencies, referred to as the BERT-CNN approach. In the literature [42], a method based on BERT-RNN for identifying emergency entities for earthquake disaster is proposed.…”

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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“…Jiang et al [31] used an irregular graph to predict the propagation time of a wildfire, while Yemshanov et al [32] tried to identify the critical nodes of a graph for effective fuel reduction treatments. Ge et al [33] built a spatiotemporal knowledge graph to predict wildfire occurrence in a test region in China, while Chen et al [34] used a similar approach to forecast the total burned area in a Portuguese national park.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Wildfire Spread Modeling of European Wildfires Using a Spatiotemporal Graph Neural Network

Rösch,

Nolde,

Ullmann

et al. 2024

Fire

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Wildfire spread models are an essential tool for mitigating catastrophic effects associated with wildfires. However, current operational models suffer from significant limitations regarding accuracy and transferability. Recent advances in the availability and capability of Earth observation data and artificial intelligence offer new perspectives for data-driven modeling approaches with the potential to overcome the existing limitations. Therefore, this study developed a data-driven Deep Learning wildfire spread modeling approach based on a comprehensive dataset of European wildfires and a Spatiotemporal Graph Neural Network, which was applied to this modeling problem for the first time. A country-scale model was developed on an individual wildfire time series in Portugal while a second continental-scale model was developed with wildfires from the entire Mediterranean region. While neither model was able to predict the daily spread of European wildfires with sufficient accuracy (weighted macro-mean IoU: Portugal model 0.37; Mediterranean model 0.36), the continental model was able to learn the generalized patterns of wildfire spread, achieving similar performances in various fire-prone Mediterranean countries, indicating an increased capacity in terms of transferability. Furthermore, we found that the spatial and temporal dimensions of wildfires significantly influence model performance. Inadequate reference data quality most likely contributed to the low overall performances, highlighting the current limitations of data-driven wildfire spread models.

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Knowledge Graph Representation Learning-Based Forest Fire Prediction

Cited by 10 publications

References 31 publications

Civil Aviation Travel Question and Answer Method Using Knowledge Graphs and Deep Learning

Civil Aviation Travel Question and Answer Method Using Knowledge Graphs and Deep Learning

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

Data-Driven Wildfire Spread Modeling of European Wildfires Using a Spatiotemporal Graph Neural Network

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