A multilayer perceptron model for the correlation between satellite data and soil vulnerability in the Ferlo, Senegal
| Autor: | Samira El Yacoubi, Waldir de Carvalho Junior, Mireille Fargette, Abdoulaye Faye, Thérèse Libourel, Maud Loireau |
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| Přispěvatelé: | UMR 228 Espace-Dev, Espace pour le développement, Université de Guyane (UG)-Université des Antilles (UA)-Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM), Centre de Suivi Ecologique [Dakar] (CSE), Empresa Brasileira de Pesquisa Agropecuária (Embrapa), Ministério da Agricultura, Pecuária e Abastecimento [Brasil] (MAPA), Governo do Brasil-Governo do Brasil |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
RESEAU NEURONAL
Computer Networks and Communications SYSTEME D'INFORMATION GEOGRAPHIQUE media_common.quotation_subject IMAGE SATELLITE Vulnerability 0102 computer and information sciences 02 engineering and technology VULNERABILITE 01 natural sciences [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Satellite data 0202 electrical engineering electronic engineering information engineering GESTION DE L'ENVIRONNEMENT media_common 2. Zero hunger DESERTIFICATION SURFACE DU SOL Soil vulnerability 15. Life on land desertification neural networks Arid MODELISATION [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Desertification 010201 computation theory & mathematics ERODIBILITE DU SOL Multilayer perceptron [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Land degradation Environmental science 020201 artificial intelligence & image processing CORRELATION Water resource management Software satellite images |
| Zdroj: | International Journal of Parallel, Emergent and Distributed Systems International Journal of Parallel, Emergent and Distributed Systems, Taylor & Francis, 2018, pp.1-10. ⟨10.1080/17445760.2018.1434175⟩ |
| ISSN: | 1744-5760 1744-5779 |
| Popis: | International audience; Soil erosion processes which contribute to desertification and land degradation, constitute major environmental and social issues for the coming decades. This is particularly true in arid areas where rural populations mostly depend on soil ability to support crop production. Assessment of soil erosion across large and quite diverse areas is very difficult but crucial for planning and management of the natural resources. The purpose of this paper is to investigate a prediction model for soil vulnerability to erosion based on the use of the information contained in satellite images. Based on neural networks models, the used approach in this paper aims at checking a correlation between the digital content of satellite images and soil vulnerability factors: erosivity (R), the soil erodibility (K), and the slope length and steepness (LS); vulnerability (V) as described in the RUSLE model. Significant results have been obtained for R and K factors. This promising pilot study was conducted in South Ferlo, Senegal, a region with Sahelian environmental characteristics. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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