Evaluating the slope behavior for geophysical flow prediction with advanced machine learning combinations
| dc.contributor.author | Kennedy C. Onyelowe | |
| dc.contributor.author | Ahmed M. Ebid | |
| dc.contributor.author | Shadi Hanandeh | |
| dc.contributor.author | Viroon Kamchoom | |
| dc.date.accessioned | 2026-05-08T19:15:03Z | |
| dc.date.issued | 2025-2-24 | |
| dc.description.abstract | of 0.946 thereby becoming the decisive intelligent model in this exercise. However, there is an advantage the deployment of GMDH, which comes second in order of superiority, has over the ANN. This is the development of a closed-form equation that allows its model to be applied manually in the design of slope stability problems. Overall, the present research models outperformed the eleven (11) models of the previous work due to sorting and elimination of unrealistic data entries deposited in the literature, the application of dimensionless combination of the studied slope stability parameters and the superiority of the selected machine learning techniques. | |
| dc.identifier.doi | 10.1038/s41598-025-90882-8 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/14840 | |
| dc.publisher | Scientific Reports | |
| dc.subject | Landslides and related hazards | |
| dc.subject | Dam Engineering and Safety | |
| dc.subject | Geotechnical Engineering and Analysis | |
| dc.title | Evaluating the slope behavior for geophysical flow prediction with advanced machine learning combinations | |
| dc.type | Article |