Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 539658
Title How can statistical and artificial intelligence approaches predict piping erosion susceptibility?
Author(s) Hosseinalizadeh, Mohsen; Kariminejad, Narges; Rahmati, Omid; Keesstra, Saskia; Alinejad, Mohammad; Mohammadian Behbahani, Ali
Source Science of the Total Environment 646 (2019). - ISSN 0048-9697 - p. 1554 - 1566.
DOI https://doi.org/10.1016/j.scitotenv.2018.07.396
Department(s) PE&RC
Alterra - Soil, water and land use
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) Loess plateau - Machine learning algorithms - Piping collapse - Susceptibility map - Unmanned aerial vehicle (UAV)
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.

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