Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology
To discuss the analysis and evaluation of highway landslides, the application of data mining methods combined with deep learning frameworks in geologic hazard evaluation and monitoring is explored preliminarily. On the premise of optimizing the processing of landslide images, first, the Blind/Refere...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/2871770 |
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| author | Feng He Chunxue Liu Hongjiang Liu |
| author_facet | Feng He Chunxue Liu Hongjiang Liu |
| author_sort | Feng He |
| collection | DOAJ |
| description | To discuss the analysis and evaluation of highway landslides, the application of data mining methods combined with deep learning frameworks in geologic hazard evaluation and monitoring is explored preliminarily. On the premise of optimizing the processing of landslide images, first, the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) based on the natural statistical characteristics of the spatial domain is introduced, which is initially combined with Super-Resolution Convolutional Neural Network (SRCNN). Then, the AlexNet is fine-tuned and applied to highway landslide monitoring and surveying. Finally, an entropy weight gray clustering evaluation method based on data mining analysis is proposed, and the performances of several methods are verified. The results show that the average score of the BRISQUE algorithm in Image Quality Assessment (IQA) is above 0.9, and the average running time is 0.1523 s. The combination of BRISQUE and SRCNN can improve the image quality significantly. After fine-tuning, the recognition accuracy of AlexNet for landslide images can reach about 80%. The evaluation method based on gray clustering can effectively determine the correlation between soil moisture content and slope angle and thereby be applied to the analysis and evaluation of highway landslides. The results are beneficial to the judgment and assessment of highway landslide conditions, which can be extended to research on other geologic hazards. |
| format | Article |
| id | doaj-art-b12a2c5141804c94b9d4623e70d16db1 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b12a2c5141804c94b9d4623e70d16db12025-02-03T06:11:57ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/28717702871770Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis TechnologyFeng He0Chunxue Liu1Hongjiang Liu2School of Urban and Environmental Sciences, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, ChinaSchool of Urban and Environmental Sciences, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, ChinaSchool of Tourism, Leshan Normal University, Sichuan, Leshan 614000, ChinaTo discuss the analysis and evaluation of highway landslides, the application of data mining methods combined with deep learning frameworks in geologic hazard evaluation and monitoring is explored preliminarily. On the premise of optimizing the processing of landslide images, first, the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) based on the natural statistical characteristics of the spatial domain is introduced, which is initially combined with Super-Resolution Convolutional Neural Network (SRCNN). Then, the AlexNet is fine-tuned and applied to highway landslide monitoring and surveying. Finally, an entropy weight gray clustering evaluation method based on data mining analysis is proposed, and the performances of several methods are verified. The results show that the average score of the BRISQUE algorithm in Image Quality Assessment (IQA) is above 0.9, and the average running time is 0.1523 s. The combination of BRISQUE and SRCNN can improve the image quality significantly. After fine-tuning, the recognition accuracy of AlexNet for landslide images can reach about 80%. The evaluation method based on gray clustering can effectively determine the correlation between soil moisture content and slope angle and thereby be applied to the analysis and evaluation of highway landslides. The results are beneficial to the judgment and assessment of highway landslide conditions, which can be extended to research on other geologic hazards.http://dx.doi.org/10.1155/2021/2871770 |
| spellingShingle | Feng He Chunxue Liu Hongjiang Liu Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology Complexity |
| title | Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology |
| title_full | Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology |
| title_fullStr | Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology |
| title_full_unstemmed | Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology |
| title_short | Integration and Fusion of Geologic Hazard Data under Deep Learning and Big Data Analysis Technology |
| title_sort | integration and fusion of geologic hazard data under deep learning and big data analysis technology |
| url | http://dx.doi.org/10.1155/2021/2871770 |
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