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Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India / Sunil Saha in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 12 n° 1 (2021) . - pp 29 - 62 Titre : Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India Type de document : Article/Communication Auteurs : Sunil Saha, Auteur ; Gopal Chandra, Auteur ; Biswajeet Pradhan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 29 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] changement d'occupation du sol [Termes IGN] classification hybride [Termes IGN] classification par Perceptron multicouche [Termes IGN] déboisement [Termes IGN] ensachage [Termes IGN] Inde [Termes IGN] modèle de simulation [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Rotation Forest classification [Termes IGN] système d'information géographique Résumé : (auteur) The rapid expansion of human settlement, agricultural land and roads because of population growth in several regions of the world has contributed to the depletion of forest land. In this study, novel ensemble intelligent approaches using bagging, dagging and rotation forest (RTF) as meta classifiers of multilayer perceptron (MLP) were used to predict spatial deforestation probability (DP) in Gumani Basin, India. The success rate and correctness of prediction of the ensemble models were compared with MLP. A total of 1000 deforested pixels and 14 deforestation determining factors (DDFs) were used. The ensemble models were trained using 70% of the deforested pixels and validated with the remaining 30%. DDFs were chosen by applying the information gain ratio and Relief-F test methods. Distance to settlement, population growth and distance to roads were the most important factors. The results of DP modelling demonstrated that nearly 16.82%–12.64% of the basin had very high DP. All four models created DP maps with reasonable prediction accuracy and goodness of fit, but the best map was produced by MLP-bagging. The accuracy of the MLP neural net model was increased 2-3% after ensemble with the hybrid meta classifiers (RTF, bagging and dagging). The proposed method could be used for deforestation prediction in other areas having similar geo-environmental conditions. Furthermore, the findings might be used as a basis for future research and could help planners in forest management. Numéro de notice : A2021-106 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2020.1860139 Date de publication en ligne : 22/12/2020 En ligne : https://doi.org/10.1080/19475705.2020.1860139 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96903 [article]

The relationship between brightness temperature and soil moisture : selection of frequency range for microwave remote sensing / K.S. Rao in International Journal of Remote Sensing IJRS, vol 8 n° 10 (October 1987)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 8 n° 10 (October 1987) . - pp 1531 - 1545 Titre : The relationship between brightness temperature and soil moisture : selection of frequency range for microwave remote sensing Type de document : Article/Communication Auteurs : K.S. Rao, Auteur ; Gopal Chandra, Auteur ; P.V.N. Rao, Auteur Année de publication : 1987 Article en page(s) : pp 1531 - 1545 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de données [Termes IGN] fréquence [Termes IGN] humidité du sol [Termes IGN] hyperfréquence [Termes IGN] image thermique [Termes IGN] température de luminance [Termes IGN] thermographie Numéro de notice : A1987-274 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431168708954795 En ligne : https://doi.org/10.1080/01431168708954795 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=24364 [article]

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080-87101	RAB	Revue	Centre de documentation	En réserve L003	Disponible