Loess Compressibility Model Predictions for Xinjiang Study Areas

This dataset compares predicted and measured loess compressibility data from a study in Huocheng and Xinyuan counties, Xinjiang. It contains results from multiple regression and machine learning models, including Random Forest, MLP, CART, and SVM. The file is 5.5 KB in size.

Use Cases

• Compare measured compressibility modulus values with model predictions from Random Forest and MLP algorithms.
• Analyze error metrics priority to evaluate the performance of SVM classification and CART models on loess compressibility parameters.
• Assess correlations between core input indicators and mechanical parameters for engineering classification in the study areas.

Strengths

• Data is licensed under CC BY 4.0, permitting open sharing and adaptation.
• The study provides results from multiple model types, including Random Forest, MLP, CART, and SVM.
• Geographic focus is specified for two counties, Huocheng and Xinyuan in Xinjiang.

Limitations

• The dataset is very small at 5.5 KB, indicating limited scope and sample size.
• No column names, row counts, or sample data are provided, making structure and volume unknown.
• The description is repetitive and lacks detailed technical specifications of the underlying data.

Provenance

Source

figshare

Collection Method

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Time Range

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Freshness

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Geography

Huocheng and Xinyuan counties, Xinjiang, China