Published

2022-02-07

Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models

Factores que afectan los umbrales topográficos en la ocurrencia de erosión y su manejo a través de modelos predictivos de aprendizaje automático

DOI:

https://doi.org/10.15446/esrj.v25n4.95748

Keywords:

boosted regression tree, erosion, prediction, reliability, support vector machine (en)
árbol de regresión optimizado; erosión; predición; confiabilidad; máquinas de vectores de soporte; (es)

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Authors

  • Mahdieh Valipour Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran
  • Neda Mohseni Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran
  • Seyed Reza Hosseinzadeh Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

Soil degradation induced by gully erosion represents a worldwide problem in the many arid and semi-arid countries, such as Iran. This study assessed: (1) the importance of variables that control gully erosion using the Boruta algorithm, (2) the relationship among causative variables and gullied locations using the evidential belief function model (EBF), and (3) gully erosion development using the algorithms of boosted regression tree (BRT) and support vector machine (SVM). Based on the results of the Boruta algorithm, slope, land use, lithology, plan curvature, and elevation were the most important factors controlling gully erosion. The results of the EBF model showed the predominance of gully erosion on rangeland and loess-marl deposition. The predominance of gullied locations on the concave positions, with the slope of 5°–20° in the vicinity of drainage lines, illustrates a preferential topographic zone and, therefore, a terrain threshold for gullying. The correlation of gullied locations with rangelands and weak soils in concave positions demonstrates that the interactions among soil characteristics, topography, and land use stimulate a low topographic threshold for gullies development. These relationships are consistent with the threshold concept that a given soil, land use, and climate within a given landscape encourage a given drainage area and a critical soil surface slope that are necessary for gully incision. Furthermore, the BRF-SVM had the highest efficiency and the lowest root mean square error, followed by BRT for predicting gully development, compared with LN-SVM algorithm. The application of two machine learning methods for predicting the gully head cut susceptibility in northern Iran showed that the maps generated by these algorithms could provide an appropriate strategy for geo-conservation and restoration efforts in gullying-prone areas.

La degradación del suelo por erosión representa un problema generalizado para aquellos países con suelos áridos y semiáridos como Iran. En este estudio se miden los siguientes aspectos: 1. La importancia de las variables que controlan la erosión a través del algoritmo de Boruta; 2. La relación entre causales y los lugares erosionados a través del modelo de confianza (EBF, del inglés evidential belief function model), y 3. desarrollo de la erosión a través de los algoritmos árboles de regresión potenciado (BRT, Boosted Regression Tree) y máquinas de vectores de soporte (SVM, support vector machine). Con base en los resultados del algoritmo de Boruta, la inclinación, el uso del suelo, la litología, la curvatura y la elevación son los factores más importantes en el control de la erosión. Los resultados del modelo de confianza muestran la predominancia de la erosión en los pastizales y en las deposiciones de marga de loess. La predominancia de lugares erosionados en puntos cóncavos, con una pendiente de entre 5 y 20 grados junto a líneas de drenaje, ejemplifica una zona topográfica preferencial y, además, un umbral en el terreno para la erosión. La correlación de zonas erosionadas con pastizales y suelos débiles en posiciones cóncavas demuestra que las interacciones entre las características del suelo, la topografía, y el estudio del suelo estimulan un umbral bajo para el desarrollo de la erosión. Estas relaciones se enmarcan en el concepto de que ante un tipo de suelo dado, el uso que se le brinde y el clima en un paisaje específico se crea una área de drenaje y una pendiente con superficie de suelo crítico, necesarios para un corte erosionado. Además, los algoritmos BRF-SVM tuvieron la mayor eficiencia y el menor error cuadrático medio, seguido por el BRT en la predicción del desarrollo de erosión frente al algortimo LN-SVM. La aplicación de dos métodos de aprendizaje automático para para predecir la susceptibilidad de corte en el norte de Irán muestra que los mapas generados por estos algortimos pueden proveer una estrategia apropiada para la geoconservación y los esfuerzos de restauración en zonas propensas a la erosión.

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APA

Valipour, M., Mohseni, N. and Hosseinzadeh, S. R. . (2022). Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models. Earth Sciences Research Journal, 25(4), 423–432. https://doi.org/10.15446/esrj.v25n4.95748

ACM

[1]
Valipour, M., Mohseni, N. and Hosseinzadeh, S.R. 2022. Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models. Earth Sciences Research Journal. 25, 4 (Feb. 2022), 423–432. DOI:https://doi.org/10.15446/esrj.v25n4.95748.

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Valipour, M.; Mohseni, N.; Hosseinzadeh, S. R. . Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models. Earth sci. res. j. 2022, 25, 423-432.

ABNT

VALIPOUR, M.; MOHSENI, N.; HOSSEINZADEH, S. R. . Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models. Earth Sciences Research Journal, [S. l.], v. 25, n. 4, p. 423–432, 2022. DOI: 10.15446/esrj.v25n4.95748. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/95748. Acesso em: 16 jan. 2025.

Chicago

Valipour, Mahdieh, Neda Mohseni, and Seyed Reza Hosseinzadeh. 2022. “Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models”. Earth Sciences Research Journal 25 (4):423-32. https://doi.org/10.15446/esrj.v25n4.95748.

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Valipour, M., Mohseni, N. and Hosseinzadeh, S. R. . (2022) “Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models”, Earth Sciences Research Journal, 25(4), pp. 423–432. doi: 10.15446/esrj.v25n4.95748.

IEEE

[1]
M. Valipour, N. Mohseni, and S. R. . Hosseinzadeh, “Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models”, Earth sci. res. j., vol. 25, no. 4, pp. 423–432, Feb. 2022.

MLA

Valipour, M., N. Mohseni, and S. R. . Hosseinzadeh. “Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models”. Earth Sciences Research Journal, vol. 25, no. 4, Feb. 2022, pp. 423-32, doi:10.15446/esrj.v25n4.95748.

Turabian

Valipour, Mahdieh, Neda Mohseni, and Seyed Reza Hosseinzadeh. “Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models”. Earth Sciences Research Journal 25, no. 4 (February 7, 2022): 423–432. Accessed January 16, 2025. https://revistas.unal.edu.co/index.php/esrj/article/view/95748.

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Valipour M, Mohseni N, Hosseinzadeh SR. Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models. Earth sci. res. j. [Internet]. 2022 Feb. 7 [cited 2025 Jan. 16];25(4):423-32. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/95748

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