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Suspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms / Marzieh Fadaee in Geocarto international, vol 37 n° 4 ([15/02/2022])
[article]
Titre : Suspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms Type de document : Article/Communication Auteurs : Marzieh Fadaee, Auteur ; Amin Mahdavi-Meymand, Auteur ; Mohammad Zounemat-Kermani, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 961 - 977 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] algorithme de Levenberg-Marquardt
[Termes IGN] algorithme génétique
[Termes IGN] analyse comparative
[Termes IGN] Indiana (Etats-Unis)
[Termes IGN] Inférence floue
[Termes IGN] modèle de simulation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificiel
[Termes IGN] sédimentRésumé : (auteur) The present study investigates the capability of two metaheuristic optimization approaches, namely the Butterfly Optimization Algorithm (BOA) and the Genetic Algorithm (GA), integrated with machine learning models in Suspended Sediment Load (SSL) prediction. The Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) are applied as the predictive data-driven models. Independent input variables, i.e., the water temperature (T), river discharge (Q), and specific conductance (SC) are used for the prediction of SSL based on several statistical indices. The results indicate that the performances of all studied models were close to one another; moreover, the metaheuristic algorithms were found to increase the accuracy of the ANFIS and ANN models for approximately 11.73 percent and 4.30 percent, respectively. In general, the BOA outperformed the GA in enhancing the optimization performance of the learning process in the applied machine learning models. Numéro de notice : A2022-392 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1753821 Date de publication en ligne : 29/07/2020 En ligne : https://doi.org/10.1080/10106049.2020.1753821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100685
in Geocarto international > vol 37 n° 4 [15/02/2022] . - pp 961 - 977[article]Analysis of factors affecting adoption of volunteered geographic information in the context of national spatial data infrastructure / Munir Ahmad in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)
[article]
Titre : Analysis of factors affecting adoption of volunteered geographic information in the context of national spatial data infrastructure Type de document : Article/Communication Auteurs : Munir Ahmad, Auteur ; Malik Sikandar Hayat Khayal, Auteur ; Ali Tahir, Auteur Année de publication : 2022 Article en page(s) : n° 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] données localisées des bénévoles
[Termes IGN] fiabilité des données
[Termes IGN] infrastructure nationale des données localisées
[Termes IGN] INSPIRE
[Termes IGN] modèle empirique
[Termes IGN] Pakistan
[Termes IGN] qualité des données
[Termes IGN] régression des moindres carrés partielsRésumé : (auteur) Spatial data infrastructures (SDIs) have been implemented for the last four decades in most countries. One of the key objectives of SDIs is to ensure the quick availability and accessibility of spatial data. The success of SDI depends on the underlying spatial datasets. Many developing countries such as Pakistan are facing problems in implementing SDI because of the unavailability of spatial data. Volunteered Geographic Information (VGI) is an alternate source for obtaining spatial data. Therefore, the question is what factors hamper the adoption of VGI for making it part of SDI in Pakistan. The intention behind this paper is to explore such factors as the key research question. To do so, we make use of the Technology–Organization–Environment (TOE) framework along with the partial least square structural equation model (PLS-SEM) to empirically analyze the factors impeding VGI from becoming part of SDI in the country. The study concludes that many technical, organizational, and environmental factors affect the adoption of VGI to be part of SDI in Pakistan. Numéro de notice : A2022-169 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11020120 En ligne : https://doi.org/10.3390/ijgi11020120 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99798
in ISPRS International journal of geo-information > vol 11 n° 2 (February 2022) . - n° 120[article]A combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)
[article]
Titre : A combination of convolutional and graph neural networks for regularized road surface extraction Type de document : Article/Communication Auteurs : Jingjing Yan, Auteur ; Shunping Ji, Auteur ; Yao Wei, Auteur Année de publication : 2022 Article en page(s) : n° 4409113 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] jeu de données
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression
[Termes IGN] réseau neuronal de graphes
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Road surface extraction from high-resolution remote sensing images has many engineering applications; however, extracting regularized and smooth road surface maps that reach the human delineation level is a very challenging task, and substantial and time-consuming manual work is usually unavoidable. In this article, to solve this problem, we propose a novel regularized road surface extraction framework by introducing a graph neural network (GNN) for processing the road graph that is preconstructed from the easily accessible road centerlines. The proposed framework formulates the road surface extraction problem as two-sided width inference of the road graph and consists of a convolutional neural network (CNN)-based feature extractor and a GNN model for vertex attribute adjustment. The CNN extracts the high-level abstract features of each vertex in the graph as the input of the GNN and also the road boundary features that allow us to distinguish roads from the background. The GNN propagates and aggregates the features of the vertices in the graph to achieve global optimization of the regression of the regularized widths of the vertices. At the same time, a biased centerline map can also be corrected based on the width prediction result. To the best of the authors’ knowledge, this is the first study to have introduced a GNN to regularized human-level road surface extraction. The proposed method was evaluated on four diverse datasets, and the results show that the proposed method comprehensively outperforms the recent CNN-based segmentation methods and other regularization methods in the intersection over union (IoU) and smoothness score, and a visual check shows that a majority of the prediction results of the proposed method approach the human delineation level. Numéro de notice : A2022-297 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3151688 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3151688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100355
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 2 (February 2022) . - n° 4409113[article]Deriving a tree growth model from any existing stand growth model / Quang V. Cao in Canadian Journal of Forest Research, Vol 52 n° 2 (February 2022)
[article]
Titre : Deriving a tree growth model from any existing stand growth model Type de document : Article/Communication Auteurs : Quang V. Cao, Auteur Année de publication : 2022 Article en page(s) : pp 137 - 147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] désagrégation
[Termes IGN] Etats-Unis
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle de croissance végétale
[Termes IGN] modélisation de la forêt
[Termes IGN] Pinus taeda
[Termes IGN] régression
[Termes IGN] surface terrièreRésumé : (auteur) In this study, a new method was developed to derive a tree survival and diameter growth model from any existing stand-level model, without the need for individual-tree growth data. Predictions from the derived tree model are constrained to match the number of trees and the basal area per hectare as outputted by the stand model. The tree models derived from three different stand models were evaluated against a tree model, in both unadjusted and disaggregated forms. For the same stand-level model, the derived tree model outperformed its counterpart, the disaggregated tree model. Furthermore, except for one stand model with poor performance, the tree models derived from the remaining two stand models delivered results comparable to those obtained from the unadjusted tree model. The tree model derived from one stand model even performed slightly better than the unadjusted tree model. This result is significant because the coefficients of the unadjusted and disaggregated tree models had to be estimated from tree-level growth data, whereas the derived tree model required no tree growth data at all. The methodology presented in this study should be applicable when there is no ingrowth or recruitment of new trees. Numéro de notice : A2022-311 Affiliation des auteurs : non IGN Autre URL associée : Draft Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1139/cjfr-2021-0106 En ligne : https://doi.org/10.1139/cjfr-2021-0106 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100408
in Canadian Journal of Forest Research > Vol 52 n° 2 (February 2022) . - pp 137 - 147[article]Efficient variance component estimation for large-scale least-squares problems in satellite geodesy / Yufeng Nie in Journal of geodesy, vol 96 n° 2 (February 2022)
[article]
Titre : Efficient variance component estimation for large-scale least-squares problems in satellite geodesy Type de document : Article/Communication Auteurs : Yufeng Nie, Auteur ; Yunzhong Shen, Auteur ; Roland Pail, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 13 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] analyse de variance
[Termes IGN] champ de pesanteur terrestre
[Termes IGN] estimation statistique
[Termes IGN] GRACE
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle stochastiqueRésumé : (auteur) Efficient Variance Component Estimation (VCE) is significant to optimal data combination in large-scale least-squares problems as those encountered in satellite geodesy, where millions of observations are jointly processed to estimate a huge number of unknown parameters. In this paper, an efficient VCE algorithm with rigorous trace calculation is proposed based on the local–global parameters partition scheme in satellite geodesy, which is directly applicable to both the simplified yet common case where local parameters are unique to a single observation group and the generalized case where local parameters are shared by different groups of observations. Moreover, the Monte-Carlo VCE (MCVCE) algorithm, based on the stochastic trace estimation technique, is further extended in this paper to the generalized case. Two numerical simulation cases are investigated for gravity field model recovery to evaluate both the accuracy and efficiency of the proposed algorithm and the extended MCVCE algorithm in terms of trace calculation. Compared to the conventional algorithm, the relative trace calculation errors in the efficient algorithm are all negligibly below 10–7%, while in the MCVCE algorithm they can vary from 0.6 to 37% depending on the number of adopted random vector realizations and the specific applications. The efficient algorithm can achieve computational time reduction rates above 96% compared to the conventional algorithm for all gravity field model sizes considered in the paper. In the MCVCE algorithm, however, the time reduction rates can change from 61 to 99% for different implementations. Numéro de notice : A2022-186 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01599-9 Date de publication en ligne : 16/02/2022 En ligne : https://doi.org/10.1007/s00190-022-01599-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99907
in Journal of geodesy > vol 96 n° 2 (February 2022) . - n° 13[article]European-wide forest monitoring substantiate the neccessity for a joint conservation strategy to rescue European ash species (Fraxinus spp.) / Jan-Peter George in Scientific reports, vol 12 (2022)PermalinkExploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods / Bin Zhang in GIScience and remote sensing, vol 59 n° 1 (2022)PermalinkMapping abundance distributions of allergenic tree species in urbanized landscapes: A nation-wide study for Belgium using forest inventory and citizen science data / Sébastien Dujardin in Landscape and Urban Planning, vol 218 (February 2022)PermalinkSymbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography / Somnath Paramanik in Applied Geography, vol 139 (February 2022)PermalinkUse of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa / Mangana Rampheri in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkCombining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)PermalinkContraintes observationnelles historiques sur la sensibilité climatique : implications pour les projections de la hausse du niveau de la mer / Jonathan Chenal (2022)PermalinkPermalinkDétection des prairies de fauche et estimation des périodes de fauche par télédétection / Emma Seneschal (2022)PermalinkEstimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)Permalink