Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data
Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogene...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/14/3496 |
| _version_ | 1827618619717058560 |
|---|---|
| author | Xingtong Ge Yi Yang Ling Peng Luanjie Chen Weichao Li Wenyue Zhang Jiahui Chen |
| author_facet | Xingtong Ge Yi Yang Ling Peng Luanjie Chen Weichao Li Wenyue Zhang Jiahui Chen |
| author_sort | Xingtong Ge |
| collection | DOAJ |
| description | Forest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogeneous data. There is a lack of a method that can effectively extract features required by machine learning-based forest fire predictions from multi-source spatio-temporal data. This paper proposes a forest fire prediction method that integrates spatio-temporal knowledge graphs and machine learning models. This method can fuse multi-source heterogeneous spatio-temporal forest fire data by constructing a forest fire semantic ontology and a knowledge graph-based spatio-temporal framework. This paper defines the domain expertise of forest fire analysis as the semantic rules of the knowledge graph. This paper proposes a rule-based reasoning method to obtain the corresponding data for the specific machine learning-based forest fire prediction methods, which are dedicated to tackling the problem with real-time prediction scenarios. This paper performs experiments regarding forest fire predictions based on real-world data in the experimental areas Xichang and Yanyuan in Sichuan province. The results show that the proposed method is beneficial for the fusion of multi-source spatio-temporal data and highly improves the prediction performance in real forest fire prediction scenarios. |
| first_indexed | 2024-03-09T10:12:09Z |
| format | Article |
| id | doaj.art-167413375220443d83c46bbceea51b54 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T10:12:09Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-167413375220443d83c46bbceea51b542023-12-01T22:39:22ZengMDPI AGRemote Sensing2072-42922022-07-011414349610.3390/rs14143496Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous DataXingtong Ge0Yi Yang1Ling Peng2Luanjie Chen3Weichao Li4Wenyue Zhang5Jiahui Chen6Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaForest fires have frequently occurred and caused great harm to people’s lives. Many researchers use machine learning techniques to predict forest fires by considering spatio-temporal data features. However, it is difficult to efficiently obtain the features from large-scale, multi-source, heterogeneous data. There is a lack of a method that can effectively extract features required by machine learning-based forest fire predictions from multi-source spatio-temporal data. This paper proposes a forest fire prediction method that integrates spatio-temporal knowledge graphs and machine learning models. This method can fuse multi-source heterogeneous spatio-temporal forest fire data by constructing a forest fire semantic ontology and a knowledge graph-based spatio-temporal framework. This paper defines the domain expertise of forest fire analysis as the semantic rules of the knowledge graph. This paper proposes a rule-based reasoning method to obtain the corresponding data for the specific machine learning-based forest fire prediction methods, which are dedicated to tackling the problem with real-time prediction scenarios. This paper performs experiments regarding forest fire predictions based on real-world data in the experimental areas Xichang and Yanyuan in Sichuan province. The results show that the proposed method is beneficial for the fusion of multi-source spatio-temporal data and highly improves the prediction performance in real forest fire prediction scenarios.https://www.mdpi.com/2072-4292/14/14/3496forest fire predictionknowledge graphspatio-temporal datamachine learningmulti-source heterogeneous data fusion |
| spellingShingle | Xingtong Ge Yi Yang Ling Peng Luanjie Chen Weichao Li Wenyue Zhang Jiahui Chen Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data Remote Sensing forest fire prediction knowledge graph spatio-temporal data machine learning multi-source heterogeneous data fusion |
| title | Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data |
| title_full | Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data |
| title_fullStr | Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data |
| title_full_unstemmed | Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data |
| title_short | Spatio-Temporal Knowledge Graph Based Forest Fire Prediction with Multi Source Heterogeneous Data |
| title_sort | spatio temporal knowledge graph based forest fire prediction with multi source heterogeneous data |
| topic | forest fire prediction knowledge graph spatio-temporal data machine learning multi-source heterogeneous data fusion |
| url | https://www.mdpi.com/2072-4292/14/14/3496 |
| work_keys_str_mv | AT xingtongge spatiotemporalknowledgegraphbasedforestfirepredictionwithmultisourceheterogeneousdata AT yiyang spatiotemporalknowledgegraphbasedforestfirepredictionwithmultisourceheterogeneousdata AT lingpeng spatiotemporalknowledgegraphbasedforestfirepredictionwithmultisourceheterogeneousdata AT luanjiechen spatiotemporalknowledgegraphbasedforestfirepredictionwithmultisourceheterogeneousdata AT weichaoli spatiotemporalknowledgegraphbasedforestfirepredictionwithmultisourceheterogeneousdata AT wenyuezhang spatiotemporalknowledgegraphbasedforestfirepredictionwithmultisourceheterogeneousdata AT jiahuichen spatiotemporalknowledgegraphbasedforestfirepredictionwithmultisourceheterogeneousdata |