Comparison of statistical algorithms for detecting homogeneous river reaches along a longitudinal continuum

Thierry Leviandier; Adrien Alber; Florence Le Ber; Hervé Piégay

hal-00640698, version 1

Comparison of statistical algorithms for detecting homogeneous river reaches along a longitudinal continuum

Thierry Leviandier ¹, Adrien Alber ², Florence Le Ber () ^3, 4, Hervé Piégay ²

Geomorphology 138, 1 (2012) 130-144

Résumé : Seven methods designed to delineate homogeneous river segments, belonging to four families, namely -- tests of homogeneity, contrast enhancing, spatially constrained classification, and hidden Markov models -- are compared, firstly on their principles, then on a case study, and on theoretical templates. These templates contain patterns found in the case study but not considered in the standard assumptions of statistical methods, such as gradients and curvilinear structures. The influence of data resolution, noise and weak satisfaction of the assumptions underlying the methods is investigated. The control of the number of reaches obtained in order to achieve meaningful comparisons is discussed. No method is found that outperforms all the others on all trials. However, the methods with sequential algorithms (keeping at order n + 1 all breakpoints found at order n) fail more often than those running complete optimisation at any order. The Hubert-Kehagias method and Hidden Markov Models are the most successful at identifying subpatterns encapsulated within the templates. Ergodic Hidden Markov Models are, moreover, liable to exhibit transition areas.

1 : Centre d'Ecologie Végétale et d'Hydrologie (CEVH)
Université Louis Pasteur - Strasbourg I – Ecole Nationale du Génie de l'Eau et de l'Environnement de Strasbourg
2 : Environnement Ville Société (EVS)
CNRS : UMR5600 – Université Jean Moulin - Lyon III – Université Lumière - Lyon II – Université Jean Monnet - Saint-Etienne – École Nationale des Travaux Publics de l'État [ENTPE] – Ecole Normale Supérieure Lettres et Sciences Humaines – Institut National des Sciences Appliquées (INSA) - Lyon
3 : Laboratoire d'Hydrologie et de Géochimie de Strasbourg (LHyGeS)
CNRS : UMR7517 – Université de Strasbourg – INSU – ENGEES
4 : ORPAILLEUR (INRIA Nancy - Grand Est / LORIA)
INRIA – CNRS : UMR7503 – Université de Lorraine

hal-00640698, version 1
http://hal.archives-ouvertes.fr/hal-00640698
oai:hal.archives-ouvertes.fr:hal-00640698
Contributeur : Florence Le Ber <>
Soumis le : Lundi 14 Novembre 2011, 10:33:00
Dernière modification le : Lundi 29 Octobre 2012, 13:24:42

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DOI : 10.1016/j.geomorph.2011.08.031

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%% hal-00640698, version 1
%% http://hal.archives-ouvertes.fr/hal-00640698
@article{leviandier:hal-00640698,
    hal_id = {hal-00640698},
    url = {http://hal.archives-ouvertes.fr/hal-00640698},
    title = {{Comparison of statistical algorithms for detecting homogeneous river reaches along a longitudinal continuum}},
    author = {Leviandier, Thierry and Alber, Adrien and Le Ber, Florence and Pi{\'e}gay, Herv{\'e}},
    abstract = {{Seven methods designed to delineate homogeneous river segments, belonging to four families, namely -- tests of homogeneity, contrast enhancing, spatially constrained classification, and hidden Markov models -- are compared, firstly on their principles, then on a case study, and on theoretical templates. These templates contain patterns found in the case study but not considered in the standard assumptions of statistical methods, such as gradients and curvilinear structures. The influence of data resolution, noise and weak satisfaction of the assumptions underlying the methods is investigated. The control of the number of reaches obtained in order to achieve meaningful comparisons is discussed. No method is found that outperforms all the others on all trials. However, the methods with sequential algorithms (keeping at order n + 1 all breakpoints found at order n) fail more often than those running complete optimisation at any order. The Hubert-Kehagias method and Hidden Markov Models are the most successful at identifying subpatterns encapsulated within the templates. Ergodic Hidden Markov Models are, moreover, liable to exhibit transition areas.}},
    keywords = {River segmentation; Spatial organisation; GIS; River continuum},
    language = {Anglais},
    affiliation = {Centre d'Ecologie V{\'e}g{\'e}tale et d'Hydrologie - CEVH , Environnement Ville Soci{\'e}t{\'e} - EVS , Laboratoire d'Hydrologie et de G{\'e}ochimie de Strasbourg - LHyGeS , ORPAILLEUR - INRIA Nancy - Grand Est / LORIA},
    pages = {130-144},
    journal = {Geomorphology},
    volume = {138},
    number = {1 },
    audience = {internationale },
    doi = {10.1016/j.geomorph.2011.08.031 },
    year = {2012},
    pdf = {http://hal.archives-ouvertes.fr/hal-00640698/PDF/delineation-accepte.pdf},
}

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