Novel deep learning algorithm in soil erodibility factor predicting at a continental scale

Soil erosion poses significant environmental and economic challenges, adversely affecting soil fertility and global agricultural productivity. We developed a novel model based on the Multi-Head Squeeze-and-Excitation Residual One-Dimensional Convolutional Neural Network (MH-SE-Res1DNet) to predict the soil erodibility factor ( K ) across Europe, representing the first application of this model for such a purpose worldwide. We conducted a comparative analysis using five benchmark machine learning algorithms, i.e., Random Forest (RF), Artificial Neural Network–Multilayer Perceptron (ANN-MLP), Support Vector Regression (SVR), Alternating Model Tree (AMT), and Pace Regression (PR), to assess the efficacy of our model. The results showed that the MH-SE-Res1DNet deep learning model had an outstanding ability for the K prediction. The model's lowest error (MAE=0.0025, RMSE=0.0031) and highest coefficient of determination (R 2 =0.943) were attained during the validation phase. Benchmark models demonstrated lower performance compared to the MH-SE-Res1DNet model, with R 2 values ranging from 0.880 to 0.912 and slightly higher errors across MAE and RMSE metrics. The sensitivity analysis of MH-SE-Res1DNet showed that its performance depends predominantly on key soil factors, particularly topsoil texture (M) and organic matter (OM) concentration. This model establishes a data-driven framework that significantly advances soil erodibility prediction by leveraging machine learning. It surpasses traditional methods and existing machine learning approaches in accuracy, efficiency, and scalability, setting a new benchmark for soil conservation planning and enabling adaptable, evidence-based land management strategies across Europe and worldwide.