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Fouille de données stochastique pour la compréhension des dynamiques temporelles et spatiales des territoires agricoles. Contribution à une agronomie numérique

Abstract : The purpose of this thesis is to develop a generic method for modelling the past and current dynamics of Landscape Organization of Farming Activity (LOFA). We developed a stochastic modelling method based on Hidden Markov Models that allows data mining within a corpus of spatio-temporal land use data to segment the corpus and reveal hidden agricultural dynamics. We applied this method to land use corpora from various sources belonging to two agricultural landscapes of regional dimension. This method provides three contributions to the modeling of LOFA : (i) LOFA description following a temporo-spatial approach that first identifies temporal regularities and then localizes them by segmenting the agricultural landscape into compact areas having similar temporal regularities; (ii) data mining of the neighborhood of land use successions and their dynamics; (iii) combining of the regularities revealed by our data mining approach at the regional level with rules identified by agronomy and ecology experts at more local scales to explain the regularities and validate the experts' hypotheses. Our results validate the hypothesis according to which LOFA fits well a Markov field of land-use successions. This thesis opens the door to a new LOFA modelling approach that investigates the combining of regularities and rules and that further exploits artificial intelligence tools. This work could serve as the beginning of what could become a numerical landscape agronomy
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https://hal.univ-lorraine.fr/tel-01749362
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El Ghali Lazrak. Fouille de données stochastique pour la compréhension des dynamiques temporelles et spatiales des territoires agricoles. Contribution à une agronomie numérique. Sciences agricoles. Université de Lorraine, 2012. Français. ⟨NNT : 2012LORR0159⟩. ⟨tel-01749362⟩

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