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Article · Zenodo · October 18, 2026

Predicting Fredlund–Xing soil-water characteristic curve parameters from grain-size and Atterberg-limit data: algorithm choice, soil grouping and input information

Aruzhan Yerzhanova, Alfrendo Satyanaga, Rezat Abishev, 이민호, Sung-Woo Moon, Jong Kim

Alfrendo Satyanaga Nio
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Abstract

Code, training data and trained models for the paper “A Controlled Benchmark of Machine-Learning Models for Soil–Water Characteristic Curve Prediction: Algorithm сhoice, Soil Grouping, and Predictor Importance” (Yerzhanova et al., Nazarbayev University). The study predicts the Fredlund and Xing (1994) SWCC parameters a, n and m from basic soil properties using ANN, GBR and RF models. Each algorithm is trained as fine-grained, coarse-grained and global models, validated on an independent set of 90 soils, and analysed for predictor importance. The repository includes the training datasets (derived from the USDA NCSS database), training and validation code, saved train/test splits, trained models and a script that predicts the full SWCC for a new soil.

Soil and Unsaturated FlowSoil Geostatistics and MappingSoil Moisture and Remote Sensingsoil water retention curveFredlund-Xing modelsoil property prediction

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