Neural Network for Predicting Soil Thermal Conductivity

Published: 12 June 2025| Version 4 | DOI: 10.17632/by2xmg7jvc.4
Contributor:
Yongwei Fu

Description

The uploaded files are the referenced dataset for the article "Neural network prediction of thermal conductivity across full saturation for various soil types" submitted to Agricultural Forest and Meteorology. It comprises two folders: 1. “Model prediction” Folder: This folder contains the trained neural network (py and pth files). It enables users to reproduce the results presented in Figure 8 (training and testing outcomes) and facilitates the application of the model to additional soil datasets. 2. “Parameters map” Folder: This folder demonstrates an example application of the trained model, where the authors used 14064 SSCBD data at surfacical horizons from WoSIS Soil Profile Database to generate and visualize the global distribution of the MLD model parameters (Sf, p, λdry and λsat).

Files

Categories

Machine Learning, Soil Physics, Neural Network, Soil Water Content

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