Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm
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| 出版年: | Applied Sciences vol. 9, no. 18 (2019), p. 3819 |
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| 第一著者: | |
| その他の著者: | , , |
| 出版事項: |
MDPI AG
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| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20190101 |b d20191231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Guo, Wenhao |u Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities & Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China; Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China | |
| 245 | 1 | |a Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm | |
| 260 | |b MDPI AG |c 2019 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term prevention and control of landslides. When the Apriori algorithm is adopted to analyze landslide data based on the MapReduce framework, numerous frequent item-sets will be generated, adversely affecting the computational efficiency. To enhance the computational efficiency of the prediction model, the IAprioriMR algorithm is proposed in this paper to enhance the efficiency of the Apriori algorithm based on the MapReduce framework by simplifying operations of the frequent item-sets. The computational efficiencies of the IAprioriMR algorithm and the original AprioriMR algorithm were compared and analyzed in the case of different data quantities and nodes, and then the efficiency of IAprioriMR algorithm was verified to be enhanced to some extent in processing large-scale data. To verify the feasibility of the proposed algorithm, the algorithm was employed in the mid-long-term early warning study of landslides in the Three Parallel Rivers. Under the same conditions, IAprioriMR algorithm of the same rule exhibited higher confidence than FP-Growth algorithm, which implied that IAprioriMR can achieve more accurate landslide prediction. This method is capable of technically supporting the prevention and control of landslides. | |
| 653 | |a Big Data | ||
| 653 | |a Datasets | ||
| 653 | |a Landslides & mudslides | ||
| 653 | |a Occupational accidents | ||
| 653 | |a Private property | ||
| 653 | |a Algorithms | ||
| 653 | |a Rivers | ||
| 653 | |a Efficiency | ||
| 653 | |a System theory | ||
| 653 | |a Geology | ||
| 700 | 1 | |a Zuo, Xiaoqing |u Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China | |
| 700 | 1 | |a Yu, Jianwei |u Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities & Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China; College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China | |
| 700 | 1 | |a Zhou, Baoding |u Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services & Research Institute for Smart Cities & Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China; Institute of Urban Smart Transportation & Safty Maintenance, Shenzhen University, Shenzhen 518060, China | |
| 773 | 0 | |t Applied Sciences |g vol. 9, no. 18 (2019), p. 3819 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2533649542/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/2533649542/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2533649542/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |