| dc.description.abstract | Landslide susceptibility analysis is crucial from hazard mitigation viewpoint. Statistical and deterministic approaches are frequently adopted for landslide susceptibility analysis. Based on physical models, deterministic approaches are superior to the statistical approaches for deterministic approaches fully taking the mechanical mechanisms into account. However, it is difficult to get the required hydraulic-mechanical parameters in a deterministic model. Back analysis is a promising way to calibrate the required parameters. Nevertheless, fewer researches really pay attention to discuss the accuracy of back analysis results. Therefore, this research uses hypothetical cases to evaluate the prospects and limitations of back analysis of regional hydraulic-mechanical parameters in a deterministic model. Three different spatial distribution types of hydraulic-mechanical parameters were assigned. Thereafter, landslide inventory, distribution of safety factor and failure probability, and pressure head of the hypothetical cases were calculated using a deterministic model, TRIGRS. These responses then used to calibrate the input parameters. The results show If we only use landslide inventory to calibrate (cohesion, friction angle, hydraulic conductivity and hydraulic diffusivity), the back calculation results which the best fit parameters are not unique and different from given parameters. The results also show if we can add hydrologic data to calibrate hydraulic parameters first, it can improve back analysis results and reduce non-uniqueness of calibrated parameters. From correlation analysis, we find the correlation coefficient between hydraulic parameters and landslide inventory is low and rainfall duration and intensity will affect it, so only use landslide inventory to calibrate hydraulic parameters is not reasonable. Calibration results can be further improved if we can obtain more event based landslide inventory maps (different intensity or duration) and combine to back analysis method.
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