Machine learning-based landslide velocity prediction model: incorporating multi- expression programming and discrete element modeling
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| Title: | Machine learning-based landslide velocity prediction model: incorporating multi- expression programming and discrete element modeling |
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| Authors: | Hasnain Gardezi, Xingyue Li, Yu Huang |
| Publisher Information: | Springer Science and Business Media LLC, 2024. |
| Publication Year: | 2024 |
| Description: | The estimation of flow parameters for gravitational flows, such as velocity, volume, and runout distance is important for disaster prevention and mitigation. In this study, we have developed a prediction model for the frontal velocity of landslides using multi-expression programming (MEP), and discrete element modeling (DEM) as a function of slope angle, slope length, volume, coefficient of energy transfer, rolling friction and static friction. Moreover, we have also determined the percentage effect of each parameter on the front velocity. The range of the values for these parameters was selected from well-documented historical cases and experimental studies. The physical modeling results indicate that the front velocity was greatly influenced by the variation in slope angle and friction parameters. The developed prediction model was validated by comparing it with various statistical indices, and by performing sensitivity analysis, which validated the experimental observations that slope angle and friction parameters control the frontal velocity by 53% and 25% respectively. Moreover, a second-level validation was carried out by comparing the predicted front velocity with the front velocity of historical rock landslide cases and found to be in good agreement. It is hoped that the proposed model will help disaster mitigation and risk assessment by effectively predicting the front velocity of the imminent slides, and also reduce the computational cost, time, and resources. |
| Document Type: | Article |
| DOI: | 10.21203/rs.3.rs-4643461/v1 |
| Rights: | CC BY |
| Accession Number: | edsair.doi...........d8cf3ce9d3b1f78cbb8da71ec4a66ce4 |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | The estimation of flow parameters for gravitational flows, such as velocity, volume, and runout distance is important for disaster prevention and mitigation. In this study, we have developed a prediction model for the frontal velocity of landslides using multi-expression programming (MEP), and discrete element modeling (DEM) as a function of slope angle, slope length, volume, coefficient of energy transfer, rolling friction and static friction. Moreover, we have also determined the percentage effect of each parameter on the front velocity. The range of the values for these parameters was selected from well-documented historical cases and experimental studies. The physical modeling results indicate that the front velocity was greatly influenced by the variation in slope angle and friction parameters. The developed prediction model was validated by comparing it with various statistical indices, and by performing sensitivity analysis, which validated the experimental observations that slope angle and friction parameters control the frontal velocity by 53% and 25% respectively. Moreover, a second-level validation was carried out by comparing the predicted front velocity with the front velocity of historical rock landslide cases and found to be in good agreement. It is hoped that the proposed model will help disaster mitigation and risk assessment by effectively predicting the front velocity of the imminent slides, and also reduce the computational cost, time, and resources. |
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| DOI: | 10.21203/rs.3.rs-4643461/v1 |