التفاصيل البيبلوغرافية
| العنوان: |
Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm. |
| المؤلفون: |
Guo, Wenhao, Zuo, Xiaoqing, Yu, Jianwei, Zhou, Baoding |
| المصدر: |
Applied Sciences (2076-3417); Sep2019, Vol. 9 Issue 18, p3819, 10p |
| مصطلحات موضوعية: |
APRIORI algorithm, LANDSLIDE prediction, LANDSLIDES, PREDICTION models, INFORMATION storage & retrieval systems |
| مصطلحات جغرافية: |
RIVERS State (Nigeria) |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder