Stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithm
Accurate and effective landslide prediction and early detection of potential geological hazards are of great importance for landslide hazard prevention and control. However, due to the hidden, sudden, and uncertain nature of landslide disasters, traditional geological survey and investigation method...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2024-11-01
|
| Series: | Electronic Research Archive |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024284 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590753987035136 |
|---|---|
| author | Shengming Hu Yongfei Lu Xuanchi Liu Cheng Huang Zhou Wang Lei Huang Weihang Zhang Xiaoyang Li |
| author_facet | Shengming Hu Yongfei Lu Xuanchi Liu Cheng Huang Zhou Wang Lei Huang Weihang Zhang Xiaoyang Li |
| author_sort | Shengming Hu |
| collection | DOAJ |
| description | Accurate and effective landslide prediction and early detection of potential geological hazards are of great importance for landslide hazard prevention and control. However, due to the hidden, sudden, and uncertain nature of landslide disasters, traditional geological survey and investigation methods are time-consuming and laborious, and it is difficult to timely and accurately investigate and predict slope stability over a large area. Machine learning approaches provide an opportunity to address this limitation. Here, we present an intelligent slope stability assessment method based on a genetic algorithm optimization of random forest algorithm (GA-RF algorithm). Based on 80 sets of typical slope samples, weight (γ), slope height (H), pore pressure value (P), cohesion force (C), internal friction angle (φ) and slope inclination angle (°) were selected as characteristic variables for slope stability evaluation. Based on the GA-RF algorithm and incorporating 10-fold cross validation, a regression prediction model is trained on the training dataset, and then regression prediction is performed on the test dataset to verify the predictive performance of the model. The results indicate that the GA-RF prediction model has decent regression performance and has certain potential for slope stability analysis. |
| format | Article |
| id | doaj-art-80b28a501826458997cacb824438aed3 |
| institution | Kabale University |
| issn | 2688-1594 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-80b28a501826458997cacb824438aed32025-01-23T07:53:00ZengAIMS PressElectronic Research Archive2688-15942024-11-0132116120613910.3934/era.2024284Stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithmShengming Hu0Yongfei Lu1Xuanchi Liu2Cheng Huang3Zhou Wang4Lei Huang5Weihang Zhang6Xiaoyang Li7National and Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, ChinaNational and Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, ChinaNational and Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, ChinaNational and Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, ChinaNational and Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, ChinaBadong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan, Hubei 430074, ChinaNational and Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, ChinaNational and Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, ChinaAccurate and effective landslide prediction and early detection of potential geological hazards are of great importance for landslide hazard prevention and control. However, due to the hidden, sudden, and uncertain nature of landslide disasters, traditional geological survey and investigation methods are time-consuming and laborious, and it is difficult to timely and accurately investigate and predict slope stability over a large area. Machine learning approaches provide an opportunity to address this limitation. Here, we present an intelligent slope stability assessment method based on a genetic algorithm optimization of random forest algorithm (GA-RF algorithm). Based on 80 sets of typical slope samples, weight (γ), slope height (H), pore pressure value (P), cohesion force (C), internal friction angle (φ) and slope inclination angle (°) were selected as characteristic variables for slope stability evaluation. Based on the GA-RF algorithm and incorporating 10-fold cross validation, a regression prediction model is trained on the training dataset, and then regression prediction is performed on the test dataset to verify the predictive performance of the model. The results indicate that the GA-RF prediction model has decent regression performance and has certain potential for slope stability analysis.https://www.aimspress.com/article/doi/10.3934/era.2024284machine learningslope stabilityprediction modelgenetic algorithmrandom forest algorithm |
| spellingShingle | Shengming Hu Yongfei Lu Xuanchi Liu Cheng Huang Zhou Wang Lei Huang Weihang Zhang Xiaoyang Li Stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithm Electronic Research Archive machine learning slope stability prediction model genetic algorithm random forest algorithm |
| title | Stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithm |
| title_full | Stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithm |
| title_fullStr | Stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithm |
| title_full_unstemmed | Stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithm |
| title_short | Stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithm |
| title_sort | stability prediction of circular sliding failure soil slopes based on a genetic algorithm optimization of random forest algorithm |
| topic | machine learning slope stability prediction model genetic algorithm random forest algorithm |
| url | https://www.aimspress.com/article/doi/10.3934/era.2024284 |
| work_keys_str_mv | AT shengminghu stabilitypredictionofcircularslidingfailuresoilslopesbasedonageneticalgorithmoptimizationofrandomforestalgorithm AT yongfeilu stabilitypredictionofcircularslidingfailuresoilslopesbasedonageneticalgorithmoptimizationofrandomforestalgorithm AT xuanchiliu stabilitypredictionofcircularslidingfailuresoilslopesbasedonageneticalgorithmoptimizationofrandomforestalgorithm AT chenghuang stabilitypredictionofcircularslidingfailuresoilslopesbasedonageneticalgorithmoptimizationofrandomforestalgorithm AT zhouwang stabilitypredictionofcircularslidingfailuresoilslopesbasedonageneticalgorithmoptimizationofrandomforestalgorithm AT leihuang stabilitypredictionofcircularslidingfailuresoilslopesbasedonageneticalgorithmoptimizationofrandomforestalgorithm AT weihangzhang stabilitypredictionofcircularslidingfailuresoilslopesbasedonageneticalgorithmoptimizationofrandomforestalgorithm AT xiaoyangli stabilitypredictionofcircularslidingfailuresoilslopesbasedonageneticalgorithmoptimizationofrandomforestalgorithm |