How can statistical and artificial intelligence approaches predict piping erosion susceptibility?

Mohsen Hosseinalizadeh*, Narges Kariminejad, Omid Rahmati, Saskia Keesstra, Mohammad Alinejad, Ali Mohammadian Behbahani

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)

Abstract

It is of fundamental importance to model the relationship between geo-environmental factors and piping erosion because of the environmental degradation attributed to soil loss. Methods that identify areas prone to piping erosion at the regional scale are limited. The main objective of this research is to develop a novel modeling approach by using three machine learning algorithms—mixture discriminant analysis (MDA), flexible discriminant analysis (FDA), and support vector machine (SVM) in addition to an unmanned aerial vehicle (UAV) images to map susceptibility to piping erosion in the loess-covered hilly region of Golestan Province, Northeast Iran. In this research, we have used 22 geo-environmental indices/factors and 345 identified pipes as predictors and dependent variables. The piping susceptibility maps were assessed by the area under the ROC curve (AUC). Validation of the results showed that the AUC for the three mentioned algorithms varied from 90.32% to 92.45%. We concluded that the proposed approach could efficiently produce a piping susceptibility map.

Original languageEnglish
Pages (from-to)1554-1566
JournalScience of the Total Environment
Volume646
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Loess plateau
  • Machine learning algorithms
  • Piping collapse
  • Susceptibility map
  • Unmanned aerial vehicle (UAV)

Fingerprint Dive into the research topics of 'How can statistical and artificial intelligence approaches predict piping erosion susceptibility?'. Together they form a unique fingerprint.

Cite this