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

Vu D.T. Faculty of Environmental Sciences, VNU University of Science, Vietnam National University, Hanoi, P405a, T2, 334 Nguyen Trai, Hanoi, Viet Nam|
Hoang N.-D. | Tran T.C. Dept. and Ins. of Civil Engineering and Environmental Informatics, Minghsin University of Science and Technology, No.1, Xinxing Rd., Xinfeng, Hsinchu, 30401, Taiwan| Cao M.-T. Faculty of Civil Engineering, Duy Tan University, P809 - 03 Quang Trung, Da Nang, 550000, Viet Nam| Tran X.-L. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam|

Measurement: Journal of the International Measurement Confederation Số , năm 2020 (Tập 164, trang -)

DOI: 10.1016/j.measurement.2020.108066

Tài liệu thuộc danh mục: ISI, Scopus

English

English

Từ khóa: Erosion; Learning algorithms; Optimization; Soils; Splines; Classification accuracy; Data-driven methods; Decision boundary; Developed model; Erosion status; Hyper-parameter; Land managements; Multivariate adaptive regression splines; Machine learning
Tóm tắt tiếng anh
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. © 2020 Elsevier Ltd

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