Machine learning based soil erosion susceptibility prediction using social spider algorithm optimized multivariate adaptive regression spline

This study proposes an advanced data-driven method which relies on the Multivariate Adaptive Regression Splines (MARS) machine learning and Social Spider Algorithm (SSA) metaheuristic for predicting soil erosion susceptibility. The MARS is employed to infer a decision boundary that separates the input data space into two distinctive regions of ‘erosion’ and ‘non-erosion’. Meanwhile, the SSA metaheuristic is aimed at optimizing the MARS performance by automatically fine-tuning its hyper-parameters. The proposed SSA optimized MARS method, named as SSAO-MARS, is trained and validated by a set of 236 samples of soil plot conditions associated with their corresponding erosion status. The research finding shows that the newly developed SSAO-MARS can attain good predictive outcomes with classification accuracy rate of roughly 96%. Therefore, the newly developed model can be a useful tool to assist land management agencies.