Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm
The prediction method plays crucial roles in the accurate prediction of rockfall runout range which could improve the protection of endangered residential areas and infrastructure. Recently, the Knearest neighbor (KNN) algorithm, one of many machine learning techniques, showed good performance in pa...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8718271/ |
| _version_ | 1818855418107002880 |
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| author | Shuai Huang Yuejun Lyu Yanju Peng Mingming Huang |
| author_facet | Shuai Huang Yuejun Lyu Yanju Peng Mingming Huang |
| author_sort | Shuai Huang |
| collection | DOAJ |
| description | The prediction method plays crucial roles in the accurate prediction of rockfall runout range which could improve the protection of endangered residential areas and infrastructure. Recently, the Knearest neighbor (KNN) algorithm, one of many machine learning techniques, showed good performance in pattern classification. Therefore, the aim of this study was to use the K-nearest neighbor (KNN) algorithm to predict the rockfall runout range which is classified into different subintervals according to the distance from the slope toe. First, we proposed the prediction model of the rockfall runout range based on our improved KNN algorithm which could better offer robustness against different choices of the neighborhood size k, and it is the first work of applying our improved KNN algorithm to rockfall runout range prediction. Second, the shaking table tests of rockfall runout models were conducted for simulating the rockfall process, and the influence laws of factors-including types of an earthquake, peak ground acceleration, vibration frequency, slope angle, slope height, and block mass and block shape-on rockfall distance are investigated. Finally, there is a discussion of the performance of our proposed prediction model based on our improved KNN algorithm in the prediction of rockfall runout range. The extensive experimental results for rockfall runout range prediction demonstrate the effectiveness of our proposed prediction model. |
| first_indexed | 2024-12-19T08:08:17Z |
| format | Article |
| id | doaj.art-f44c98e888414a64ada6fe93ce08e8b0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:08:17Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f44c98e888414a64ada6fe93ce08e8b02022-12-21T20:29:42ZengIEEEIEEE Access2169-35362019-01-017667396675210.1109/ACCESS.2019.29178688718271Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN AlgorithmShuai Huang0https://orcid.org/0000-0002-9081-2760Yuejun Lyu1Yanju Peng2Mingming Huang3Institute of Crustal Dynamics, China Earthquake Administration, Beijing, ChinaInstitute of Crustal Dynamics, China Earthquake Administration, Beijing, ChinaInstitute of Crustal Dynamics, China Earthquake Administration, Beijing, ChinaBeijing Meteorological Information Center, Beijing, ChinaThe prediction method plays crucial roles in the accurate prediction of rockfall runout range which could improve the protection of endangered residential areas and infrastructure. Recently, the Knearest neighbor (KNN) algorithm, one of many machine learning techniques, showed good performance in pattern classification. Therefore, the aim of this study was to use the K-nearest neighbor (KNN) algorithm to predict the rockfall runout range which is classified into different subintervals according to the distance from the slope toe. First, we proposed the prediction model of the rockfall runout range based on our improved KNN algorithm which could better offer robustness against different choices of the neighborhood size k, and it is the first work of applying our improved KNN algorithm to rockfall runout range prediction. Second, the shaking table tests of rockfall runout models were conducted for simulating the rockfall process, and the influence laws of factors-including types of an earthquake, peak ground acceleration, vibration frequency, slope angle, slope height, and block mass and block shape-on rockfall distance are investigated. Finally, there is a discussion of the performance of our proposed prediction model based on our improved KNN algorithm in the prediction of rockfall runout range. The extensive experimental results for rockfall runout range prediction demonstrate the effectiveness of our proposed prediction model.https://ieeexplore.ieee.org/document/8718271/Improved KNN algorithmrockfall runout rangeearthquakeshaking table test |
| spellingShingle | Shuai Huang Yuejun Lyu Yanju Peng Mingming Huang Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm IEEE Access Improved KNN algorithm rockfall runout range earthquake shaking table test |
| title | Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm |
| title_full | Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm |
| title_fullStr | Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm |
| title_full_unstemmed | Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm |
| title_short | Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm |
| title_sort | analysis of factors influencing rockfall runout distance and prediction model based on an improved knn algorithm |
| topic | Improved KNN algorithm rockfall runout range earthquake shaking table test |
| url | https://ieeexplore.ieee.org/document/8718271/ |
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