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

Dinh Tuan Vu, Xuan Linh Tran, Minh Tu Cao, Thien Cuong Tran, Nhat Duc Hoang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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.

Original languageEnglish
Article number108066
JournalMeasurement: Journal of the International Measurement Confederation
Volume164
DOIs
StatePublished - Nov 2020

Keywords

  • Hybrid machine learning
  • Multivariate adaptive regression splines
  • Social spider algorithm
  • Soil erosion

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