A method of site parameter estimation based on decision tree theory considering terrain features

The development of site parameter V-S30 estimation method based on surface terrain features has become a hot topic because of its numerous application demands. The DEM data and engineering boreholes collected from Xinjiang Uygur Autonomous Region and Hebei Province are used to validate whether the V-S30 estimation method based on decision tree theory considering terrain features is applicable to China, and testify its accuracy in V-S30 prediction and sensitivity to DEM data resolution. The following conclusions are drawn: (1) Based on the decision tree theory both regions are classified as 16 types of terrain categories by means of three terrain features, i.e., topographic slope, surface texture and local convexity, and the V-S30 prediction models are developed considering proxies of these three terrain features; (2) It is validated that the V-S30 estimation method based on decision tree theory considering terrain features has universal application, but is regionally dependent and calls for developing V-S30 prediction models separately; (3) It has been observed that the accuracy of V-S30 prediction is improved after introducing two terrain features (i.e., surface texture and local convexity) on the basis of topographic slope; (4) Terrain classification is sensitive to DEM data resolution, and high-resolution data is more appropriate for steep mountain areas, while low-resolution data results in more detailed classifications in plain areas. In comparison, the 900m-resolution DEM data is relatively more practical for terrain-proxy methods in V-S30 prediction. The proposed models and methods in this study could support for developing an effective technical approach for the development of regional site classification maps in China.

Automated summary

This study developed a V-S30 parameter estimation method based on surface terrain features and validated its applicability in China. The results showed that the method has universal application but requires region-specific adjustments and development. Additionally, the inclusion of surface texture and local convexity improved the accuracy of V-S30 prediction.