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An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas / M.K. Arora in International Journal of Remote Sensing IJRS, vol 25 n° 3 (February 2004)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 3 (February 2004) . - pp 559 - 572 Titre : An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas Type de document : Article/Communication Auteurs : M.K. Arora, Auteur ; A.S. Das Gupta, Auteur ; Ravi P. Gupta, Auteur Année de publication : 2004 Article en page(s) : pp 559 - 572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] cartographie des risques [Termes IGN] effondrement de terrain [Termes IGN] Himalaya [Termes IGN] image IRS [Termes IGN] réseau neuronal artificiel [Termes IGN] risque naturel [Termes IGN] système d'information géographique [Termes IGN] zone à risque Résumé : (Auteur) Landslides are natural hazards that cause havoc to both property and life every year, especially in the Himalayas. Landslide hazard zonation (LHZ) of areas affected by landslides therefore is essential for future developmental planning and organization of various disaster mitigation programmes. The conventional Geographical Information System (GIS)-based approaches for LHZ suffer from the subjective weight rating system where weights are assigned to different causative factors responsible for triggering a landslide. Alternatively, artificial neural networks (ANNs) may be applied. These are considered to be independent of any strict assumptions or bias, and they determine the weights objectively in an iterative fashion. In this study, an ANN has been applied to generate an LHZ map of an area in the Bhagirathi Valley, Himalayas, using spatial data prepared from IRS-IB satellite sensor data and maps from other sources. The accuracy of the LHZ map produced by the ANN is around 80% with a very small training dataset. The distribution of landslide hazard zones derived from ANN shows similar trends as that observed with the existing landslides locations in the field. A comparison of the results with an earlier produced GIS-based LHZ map of the same area by the authors (using the ordinal weight rating method) indicates that ANN results are better than the earlier method. Numéro de notice : A2004-063 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000156819 En ligne : https://doi.org/10.1080/0143116031000156819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26591 [article]

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