A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution.

ABSTRACT: Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil management. However, traditional soil analysis methods often rely on statistical methods, which means they always rely on specific assumptions and are sensitive to outliers. Existing machine learning based methods convert features containing spatial information into one-dimensional vectors, resulting in the loss of some spatial features of the data. This study explores the application of Three-Dimensional Convolutional Neural Networks (3DCNN) in spatial interpolation to evaluate soil pollution. By introducing Channel Attention Mechanism (CAM), the model assigns different weights to auxiliary variables, improving the prediction accuracy of soil hy

SUBMITTER: Miao S 

PROVIDER: S-EPMC11759995 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature