@article{fdi:010072589,
  title = {{A} multilayer perceptron model for the correlation between satellite data and soil vulnerability in the {F}erlo, {S}enegal},
  author = {{E}l {Y}acoubi, {S}. and {F}argette, {M}ireille and {F}aye, {A}. and {D}e {C}arvalho, {W}. {J}r and {L}ibourel, {T}. and {L}oireau, {M}aud},
  editor = {},
  language = {{ENG}},
  abstract = {{S}oil erosion processes which contribute to desertification and land degradation, constitute major environmental and social issues for the coming decades. {T}his is particularly true in arid areas where rural populations mostly depend on soil ability to support crop production. {A}ssessment of soil erosion across large and quite diverse areas is very difficult but crucial for planning and management of the natural resources. {T}he 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. {B}ased 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. {S}ignificant results have been obtained for {R} and {K} factors. {T}his promising pilot study was conducted in {S}outh {F}erlo, {S}enegal, a region with {S}ahelian environmental characteristics.},
  keywords = {{DESERTIFICATION} ; {ERODIBILITE} {DU} {SOL} ; {SURFACE} {DU} {SOL} ; {IMAGE} {SATELLITE} ; {CORRELATION} ; {MODELISATION} ; {GESTION} {DE} {L}'{ENVIRONNEMENT} ; {SYSTEME} {D}'{INFORMATION} {GEOGRAPHIQUE} ; {RESEAU} {NEURONAL} ; {VULNERABILITE} ; {SENEGAL} ; {ZONE} {SAHELIENNE} ; {FERLO}},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {P}arallel, {E}mergent and {D}istributed {S}ystems},
  volume = {34},
  numero = {1},
  pages = {art. no 1434175 [ 3--12]},
  ISSN = {1744-5760},
  year = {2019},
  URL = {https://www.documentation.ird.fr/hor/fdi:010072589},
}