# Correlation analysis and prediction models for loess compressibility in Ili region, Xinjiang

## TL;DR

This study analyzes and predicts loess compressibility in Xinjiang's Ili region using statistical and machine learning methods to improve engineering assessments.

## Contribution

The study introduces a machine learning-based approach for predicting loess compressibility in the Ili region.

## Key findings

- Compression coefficient is positively correlated with void ratio and negatively with dry density and compressibility modulus.
- MLP and Random Forest models showed the best performance in predicting the compression coefficient.
- The study provides a method for rapid estimation of loess compressibility parameters in the Ili region.

## Abstract

Loess compressibility is a crucial engineering parameter governing the deformation of loess foundations and the evolution of slope geohazards. Based on a comprehensive collection of physical, hydraulic, and mechanical parameters of loess in the Ili region, this study selected Huocheng, Nilka, and Xinyuan counties as typical study areas. Statistical methods were employed to a perform normality tests and necessary transformations on the data, followed by correlation analysis to identify key factors influencing the compression coefficient. Using Multiple Linear Regression (MLR) as a baseline, six machine learning models were constructed, including Random Forest (RF), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Machine (SVM), Classification and Regression Tree (CART), and XGBoost models. The results indicate that the compression coefficient is significantly positively correlated with the void ratio and negatively correlated with dry density and compressibility modulus. Consequently, compressibility modulus, dry density, and void ratio were selected as core input indicators. All constructed models successfully predicted the compression coefficient and its engineering classification. Under the evaluation principle of “error metrics priority, classification accuracy auxiliary,” the MLP model achieved the best overall performance across the three counties, followed by the Random Forest model. This study provides a methodological basis for the rapid estimation of loess compressibility parameters and engineering judgment in the Ili region.