[en] Soil erosion by gullying causes severe soil degradation, which in turn leads to severe socio-economic and environmental damages in tropical and subtropical regions. To mitigate these negative effects and guarantee sustainable management of natural resources, gullies must be prevented. Gully management strategies start by devising adequate assessment tools and identification of driving factors and control measures. To achieve this, machine learning methods are essential tools to assist in the identification of driving factors to implement site-specific control measures. This study aimed at assessing the effectiveness of four machine learning methods (Random Forest (RF), Maximum of Entropy (MaxEnt), Artificial Neural Network (ANN), and Boosted Regression Tree (BRT)) to identify gully's driving factors, and predict gully erosion susceptibility in the Luzinzi watershed, in eastern Democratic Republic of Congo (DRC).
Research Center/Unit :
Laplec UR Spheres
Disciplines :
Earth sciences & physical geography
Author, co-author :
Chuma Basimine, Géant ; Université de Liège - ULiège > Sphères ; Université évangélique en Afrique
Mugumaarhahama, Yannick,; Université évangélique en Afrique
Mondo Mubalama, Jean; Université évangélique en Afrique
Bagula Mukengere, Espoir; Université évangélique en Afrique
Ndeko Byamungu, Adrien; Université évangélique en Afrique
Lucungu, Prince Baraka; Université évangélique en Afrique
Katcho, Karume; Université évangélique en Afrique
Mushagalusa Nachigera, Gustave; Université évangélique en Afrique
Schmitz, Serge ; Université de Liège - ULiège > Département de géographie > Service de géographie rurale (Laboratoire pour l'analyse des lieux, des paysages et des campagnes européennes LAPLEC)
Language :
English
Title :
Gully erosion susceptibility mapping using four machine learning methods in Luzinzi watershed, eastern Democratic Republic of Congo
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