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Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping / Emrehan Kutlug Sahin in Geocarto international, vol 37 n° 9 ([15/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 9 [15/05/2022] . - pp 2441 - 2465 Titre : Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Emrehan Kutlug Sahin, Auteur Année de publication : 2022 Article en page(s) : pp 2441 - 2465 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse comparative [Termes IGN] cartographie thématique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] effondrement de terrain [Termes IGN] Extreme Gradient Machine [Termes IGN] khi carré [Termes IGN] Turquie Résumé : (auteur) The aim of the study is to compare four recent gradient boosting algorithms named as Gradient Boosting Machine (GBM), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) for modelling landslide susceptibility (LS). In the first step of the study, the geodatabase including landslide inventory map and landslide conditioning factors was constructed. In the second step, chi-square (CHI) statistic-based feature selection (FS) technique was utilized to compute the importance of the landslide causative factors. In the third step, tree-based ensemble learning algorithms were applied to predict the potential distribution of landslide susceptibility. Also, the prediction performance of ensemble methods was compared to that of Random Forest (RF) ensemble method. Finally, the prediction capabilities of the methods were assessed using overall accuracy (Acc), area under the receiver operating characteristic curve (AUC), kappa index, root mean square error (RMSE), and F score measures. In order to further evaluation, the McNemar's test was utilized to assess statistical significance in the differences between the four gradient boosting models. The accuracy results indicated that the CatBoost model had the highest prediction capability (Acc= 0.8503 and AUC= 0.8975), followed by the XGBoost (Acc= 0.8336 and AUC= 0.8860), the LightGBM (Acc= 0.8244 and AUC= 0.8796) and the GBM (Acc= 0.8080 and AUC= 0.8685). On the other hand, the estimated accuracy measures considered in this study showed that the RF method had the lowest prediction capability of compared the others. Although the individual performances of the methods were found to be acceptable level, the CatBoost method showed the superior performance compared to others with respect to the AUC and Acc values estimated in this study. The results of the study confirmed that the relatively new ensemble learning techniques were efficient and robust for producing LS maps and furthermore, it is probably that these algorithms will be preferred more often in the future studies due to their robustness. Numéro de notice : A2022-564 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1831623 Date de publication en ligne : 16/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1831623 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101244 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2022091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Detection and mapping of snow avalanche debris from Western Himalaya, India using remote sensing satellite images / Kamal Kant Singh in Geocarto international, vol 37 n° 9 ([15/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 9 [15/05/2022] . - pp 2561 - 2579 Titre : Detection and mapping of snow avalanche debris from Western Himalaya, India using remote sensing satellite images Type de document : Article/Communication Auteurs : Kamal Kant Singh, Auteur ; Dhiraj Kumar Singh, Auteur ; Narinder Kumar Thakur, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2561 - 2579 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] avalanche [Termes IGN] Himalaya [Termes IGN] image Sentinel-MSI [Termes IGN] matrice de co-occurrence [Termes IGN] modèle numérique de surface [Termes IGN] réflectance [Termes IGN] signature spectrale Résumé : (auteur) Release of snow avalanche from a mountain slope depends on various parameters such as snow cover, terrain and meteorological conditions of the region. The precise information of avalanche occurrence in terms of its location and extent is essentially important for hazard mapping and for avalanche occurrence feedback. In the present study, various techniques have been explored for automatic detection and mapping of snow avalanche debris for a part of Western Himalayan region using Sentinel-2 satellite data. Spectral signatures of avalanche and non-avalanche snow collected from the field spectroradiometer survey are used for identifying suitable spectral bands of Sentinel-2 for avalanche debris detection. Techniques such as Ratio Method, Gray Level Co-occurrence Matrix, a new proposed index, i.e. Avalanche Debris Index and Object-Based Image Analysis (OBIA) are applied on satellite images to retrieve the avalanche debris. Retrieved avalanche debris are further compared with the manually digitized avalanche occurred boundaries. The OBIA method has been found the most suitable for avalanche debris detection and mapping using the medium resolution satellite data. Numéro de notice : A2022-565 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1762762 Date de publication en ligne : 26/05/2020 En ligne : https://doi.org/10.1080/10106049.2020.1762762 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101245 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2022091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spatial-temporal variation of satellite-based gross primary production estimation in wheat-maize rotation area during 2000–2015 / Wenquan Xie in Geocarto international, vol 37 n° 9 ([15/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 9 [15/05/2022] . - pp 2506 - 2523 Titre : Spatial-temporal variation of satellite-based gross primary production estimation in wheat-maize rotation area during 2000–2015 Type de document : Article/Communication Auteurs : Wenquan Xie, Auteur ; Huini Wang, Auteur ; Hong Chi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2506 - 2523 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] blé (céréale) [Termes IGN] Chine [Termes IGN] image Terra-MODIS [Termes IGN] maïs (céréale) [Termes IGN] photosynthèse [Termes IGN] production primaire brute [Termes IGN] rotation de culture [Termes IGN] série temporelle [Termes IGN] variation temporelle Résumé : (auteur) North China Plain is the largest agricultural production center in China and wheat-maize rotation is a widespread cultivation practice in this area. As gross primary production (GPP) is a proxy of land productivity, research on its spatial-temporal dynamics helps understand the variation of grain production in wheat-maize rotation. Here, Moderate Resolution Imaging Spectroradiometer (MODIS) data and ground observation data were combined to drive Vegetation Photosynthesis Model (VPM) in GPP estimation over wheat-maize rotation area during 2000–2015. Annual GPP has increased by 540.95 g C m−2 year−1 from 2000 to 2015, while total annual GPP has grown ∼150% than that of 2000. Moreover, annual GPP showed an increasing trend in the consecutively wheat-maize rotation area between 2000 and 2015. A strong linear relationship between GPP estimates and grain production demonstrated the potential of using VPM model to evaluate grain production in wheat-maize rotation area of Henan province, China. Numéro de notice : A2022-566 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1822928 Date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.1080/10106049.2020.1822928 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101249 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2022091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Novel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping / A'Kif Al-Fugara in Geocarto international, vol 37 n° 9 ([15/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 9 [15/05/2022] . - pp 2627 - 2646 Titre : Novel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping Type de document : Article/Communication Auteurs : A'Kif Al-Fugara, Auteur ; Mohammad Ahmadlou, Auteur ; Rania Shatnawi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2627 - 2646 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] algorithme du recuit simulé [Termes IGN] algorithme génétique [Termes IGN] analyse comparative [Termes IGN] carte hydrogéologique [Termes IGN] eau souterraine [Termes IGN] Jordanie [Termes IGN] méthode heuristique [Termes IGN] optimisation (mathématiques) [Termes IGN] régression Résumé : (auteur) This study aims to develop three novel GIS-based models combining Genetic Algorithm (GA), Biogeography-Based Optimization (BBO) and Simulated Annealing (SA) with Support Vector Regression (SVR) for groundwater potential (GP) mapping in the governorate of Tafillah, Jordan. Twelve topographical, hydrological and geological factors were considered. The mapping process was done with and without feature selection (FS) conducted by integration of SVR model with GA, BBO and SA algorithms. The accuracy of these models was evaluated using the area under receiver operating characteristic (AUROC) curve. Comparisons among the models uncovered that the SVR-RBF-GA and SVR-RBF-BBO models performed better than the SVR-RBF-SA. The AUROC for two mentioned models were 0.964 and 0.996 in training and testing runs, respectively, while this metric was 0.953 and 0.986 for SVR-RBF-SA model in training and testing runs, respectively. The results showed that after FS, the models are more accurate in test data than train data. Numéro de notice : A2022-567 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1831622 Date de publication en ligne : 19/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1831622 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101250 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2022091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A new method to detect targets in hyperspectral images based on principal component analysis / Shahram Sharifi Hashjin in Geocarto international, vol 37 n° 9 ([15/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 9 [15/05/2022] . - pp 2679 - 2697 Titre : A new method to detect targets in hyperspectral images based on principal component analysis Type de document : Article/Communication Auteurs : Shahram Sharifi Hashjin, Auteur ; Safa Khazai, Auteur Année de publication : 2022 Article en page(s) : pp 2679 - 2697 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse en composantes principales [Termes IGN] détection de cible [Termes IGN] estimation de cohérence [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale Résumé : (auteur) Target detection (TD) is a major task in hyperspectral image (HSI) processing which, due to the high spectral resolution, requires dealing with the curse of dimensionality. The integrated feature extraction and selection is a well-known solution for dimensionality reduction of HSIs. In this study, a new method is presented to improve the performance of TD algorithms based on principal component analysis (PCA) feature space. In this method, using the implantation of the target spectrum (TS) in the HSI and following the simulated targets in the PCA feature space, the best principal components (PCs) are selected. Then, using the mixing and unmixing coefficients of the PCs, a new TS and a new image in the PCA feature space are created. Afterwards, using the new spectrum of the target, the TD algorithm is run on the new HSI. The performance of the proposed method is compared to nine counterpart algorithms on Hymap and Hyperion HSI. All the comparisons are performed using adaptive coherence estimator (ACE) TD algorithm. Experimental results illustrate that the proposed method, compared to its counterparts, yields superior performance based on the false alarm rate (FAR) measure. It gives an average FAR value of about 16, which is approximately 9% better than that of its best counterparts. Numéro de notice : A2022-568 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1831625 Date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.1080/10106049.2020.1831625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101251 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2022091	RAB	Revue	Centre de documentation	En réserve L003	Disponible