Prediction of current and future gully erosion susceptibility in the Oued Zat watershed in the High Atlas region of Morocco using an SVM-based machine learning approach

Author:

Dallahi Youssef12ORCID,Malaainine Mohamed El Imame1,Dansou Caleb3,Hbiak Ishak1,Boujraf Amal4,Orlando Collins Ashianga3ORCID

Affiliation:

1. Equipe Sciences Géomatiques (SGEO), Laboratoire du Génie des Systèmes (LAGES), Ecole Hassania des Travaux Publics (EHTP), Casablanca, Morocco

2. Laboratoire de Biotechnologie et Physiologie Végétales, Centre de Biotechnologie Végétale et Microbienne Biodiversité et Environnement, Faculté des Sciences, Université Mohammed V de Rabat, Rabat, Morocco

3. Independent Researcher, Sale, Morocco

4. Laboratoire des Productions Végétale, Animales et Agro-industrie, Equipe de Botanique, Biotechnologie et Protection des Plantes, Faculté des Sciences, Université Ibn Tofail, Kénitra, Morocco

Publisher

Informa UK Limited

Reference114 articles.

1. Soil erosion and degradation assessment integrating multi-parametric methods of RUSLE model, RS, and GIS in the Shaqlawa agricultural area, Kurdistan Region, Iraq

2. Abdulfatai I. A. A. I. Okunlola W. G. Akande L. O. Momoh and K. O. Ibrahim. 2014. Review of gully erosion in Nigeria: Causes impacts and possible solutions. Journal of Geosciences and Geomatics 2 (3):125–9. doi: 10.12691/jgg-2-3-8

3. Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms

4. An interrogation of research on the influence of rainfall on gully erosion

5. Apostolidis-Afentoulis V. and K. I. Lioufi. 2015. SVM classification with linear and RBF kernels. July 0-7. http://www. academia. edu/13811676.

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