Loess Compressibility Regression Coefficients for Xinjiang Soils

Regression coefficients and significance analysis for loess compressibility and soil properties. The dataset includes results from models like Random Forest and SVM, with a focus on study areas in Huocheng and Xinyuan counties, Xinjiang. It is a small dataset at 5.5 KB, with an unknown number of rows and columns.

Use Cases

• Analyze the relationship between loess compressibility parameters and mechanical parameters using the provided regression coefficients.
• Validate model performance by comparing error metrics priority across methods like Random Forest, SVM, and CART.
• Assess the correlation significance (e.g., significantly positively correlated or negatively correlated) between core input indicators and compressibility modulus.

Strengths

• Includes results from multiple analytical models such as Random Forest, SVM, and MLP.
• Focuses on specific geographic study areas: Huocheng and Xinyuan counties in Xinjiang.
• Dataset is openly licensed under CC BY 4.0.

Limitations

• Very small dataset size of 5.5 KB suggests limited scope and potentially few data points.
• Unknown row and column counts prevent assessment of statistical power or feature richness.
• Tags suggest a focus on model outputs rather than raw observational soil data, limiting direct geotechnical analysis.

Provenance

Source

figshare

Collection Method

Analysis output from a study on loess compressibility, likely involving regression and machine learning models.

Freshness

Last updated on 2026-03-23.

Geography

Huocheng and Xinyuan counties, Xinjiang, China.