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The use of gravity data to determine orthometric heights at the Hong Kong territories / Albertini Nsiah Ababio in Journal of applied geodesy, vol 16 n° 4 (October 2022)
[article]
Titre : The use of gravity data to determine orthometric heights at the Hong Kong territories Type de document : Article/Communication Auteurs : Albertini Nsiah Ababio, Auteur ; Robert Tenzer, Auteur Année de publication : 2022 Article en page(s) : pp 401 - 416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] altitude orthométrique
[Termes IGN] correction orthométrique
[Termes IGN] Hong-Kong
[Termes IGN] interpolation
[Termes IGN] levé gravimétrique
[Termes IGN] montagne
[Termes IGN] système de référence local
[Vedettes matières IGN] AltimétrieRésumé : (auteur) The Hong Kong Principal Datum (HKPD) is the currently adopted official geodetic vertical datum at the Hong Kong territories. The HKPD is practically realized by heights of levelling benchmarks. The HKPD heights are, however, neither normal nor orthometric. The reason is that heights of levelling benchmarks were determined from precise levelling measurements, but without involving gravity observations along levelling lines. To reduce systematic errors due to disregarding the gravity information along levelling lines, we used terrestrial and marine gravity data to interpolate gravity values at levelling benchmarks in order to compute and apply the orthometric correction to measured levelling height differences. Our results demonstrate the importance of incorporating the gravity information even for a relatively small region but characterized by a rough topography with heights of levelling benchmarks exceeding several hundreds of meters. According to our estimates, the orthometric correction reaches (and even slightly exceeds) ±2 cm, with maxima along levelling lines crossing mountain chains. Numéro de notice : A2022-742 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2022-0012 Date de publication en ligne : 04/08/2022 En ligne : https://doi.org/10.1515/jag-2022-0012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101724
in Journal of applied geodesy > vol 16 n° 4 (October 2022) . - pp 401 - 416[article]A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers / Qasim Khan in Geocarto international, vol 37 n° 20 ([20/09/2022])
[article]
Titre : A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers Type de document : Article/Communication Auteurs : Qasim Khan, Auteur ; Muhammad Usman Liaqat, Auteur ; Mohamed Mostafa Mohamed, Auteur Année de publication : 2022 Article en page(s) : pp 5832 - 5850 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage automatique
[Termes IGN] aquifère
[Termes IGN] ArcGIS
[Termes IGN] classification bayesienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] eau souterraine
[Termes IGN] Emirats Arabes Unis
[Termes IGN] estimation par noyau
[Termes IGN] nitrate
[Termes IGN] vulnérabilitéRésumé : (auteur) Groundwater is more prone to contamination due to its extensive usage. Different methods are applied to study vulnerability of groundwater including widely used DRASTIC method, SI and GOD. This study proposes a novel method of mapping groundwater vulnerability using machine learning algorithms. In this study, point extraction method was used to extract point values from a grid of 646 points of seven raster layer in the Al Khatim study area of United Arab Emirates. These extracted values were classified based on nitrate concentration threshold of 50 mg/L into two classes. Machine learning models were developed, using depth to water (D), recharge (R), aquifer media (A), soil media (S), topography (T), vadose zone (I) and hydraulic conductivity (C), on the basis of nitrate class. Classified ‘groundwater vulnerability class values’ were trained using 10-fold cross-validation, using four machine learning models which were Random Forest, Support Vector Machine, Naïve Bayes and C4. 5. Accuracy showed the model developed by Random Forest gained highest accuracy of 93%. Four groundwater vulnerability maps were developed from machine learning classifiers and was compared with base method of DRASTIC index. The efficiency, accuracy and validity of machine learning based models were evaluated based on Receiver Operating Characteristics (ROC) curve and Precision-Recall curve (PRC). The results proved that machine learning is an efficient tool to access, analyze and map groundwater vulnerability. Numéro de notice : A2022-716 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1923833 Date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1923833 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101641
in Geocarto international > vol 37 n° 20 [20/09/2022] . - pp 5832 - 5850[article]Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])
[article]
Titre : Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood Type de document : Article/Communication Auteurs : Amid Darabi, Auteur ; Omid Rahmati, Auteur ; Seyed Amir Naghibi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 5716 - 5741 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] aléa
[Termes IGN] apprentissage automatique
[Termes IGN] cartographie des risques
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] écoulement des eaux
[Termes IGN] inondation
[Termes IGN] Iran
[Termes IGN] simulation spatiale
[Termes IGN] zone urbaineRésumé : (auteur) In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models. Numéro de notice : A2022-708 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1920629 Date de publication en ligne : 13/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1920629 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101578
in Geocarto international > vol 37 n° 19 [15/09/2022] . - pp 5716 - 5741[article]Prediction of suspended sediment concentration using hybrid SVM-WOA approaches / Sandeep Samantaray in Geocarto international, vol 37 n° 19 ([15/09/2022])
[article]
Titre : Prediction of suspended sediment concentration using hybrid SVM-WOA approaches Type de document : Article/Communication Auteurs : Sandeep Samantaray, Auteur ; Abinash Sahoo, Auteur Année de publication : 2022 Article en page(s) : pp 5609 - 5635 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] alluvion
[Termes IGN] bassin hydrographique
[Termes IGN] fonction de base radiale
[Termes IGN] Inde
[Termes IGN] modèle de simulation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] optimisation par essaim de particules
[Termes IGN] régression
[Termes IGN] sédiment
[Termes IGN] séparateur à vaste margeRésumé : (auteur) Suspended sediment concentration (SSC) is one of the primary reasons with respect to watersheds or river basins, which must be assessed in a correct manner so that it will help decision makers to make right decisions regarding hydraulic structure, flash-flood, flood-mitigation of the basin. The present research evaluated efficacy of a hybrid model integrating Support Vector Machine with Whale optimization algorithm (SVM-WOA) for predicting SSC at Sundargarh and Salebhata stations in Mahanadi River, India. Various quantitative statistical evaluation constrains are applied to evacuate the model performance. Also, model performance of SVM-WOA is compared with SVM-PSO (Particle Swarm Optimization) and conventional SVM and RBFN (Radial Basis Function Network) models. The results reveal that, SVM-WOA performed superiorly in comparison to SVM-PSO, SVM and RBFN models for five different input scenarios during both training and testing phases. Hence, it is recommended to apply SVM-WOA as an appropriate technique for hydrological simulation at the basin. Numéro de notice : A2022-707 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1920638 Date de publication en ligne : 17/05/2021 En ligne : https://doi.org/10.1080/10106049.2021.1920638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101577
in Geocarto international > vol 37 n° 19 [15/09/2022] . - pp 5609 - 5635[article]Adaptive block modeling of time dependent variations of datum reference points in a tectonically active area / Chun-Yun Chou in Survey review, vol 54 n° 386 (September 2022)
[article]
Titre : Adaptive block modeling of time dependent variations of datum reference points in a tectonically active area Type de document : Article/Communication Auteurs : Chun-Yun Chou, Auteur ; Jen-Yu Han, Auteur Année de publication : 2022 Article en page(s) : pp 404 - 419 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] analyse de groupement
[Termes IGN] angle d'Euler
[Termes IGN] champ de vitesse
[Termes IGN] Cinématique
[Termes IGN] collocation par moindres carrés
[Termes IGN] données GNSS
[Termes IGN] formule d'Euler
[Termes IGN] matrice de covariance
[Termes IGN] rotation
[Termes IGN] série temporelle
[Termes IGN] station GNSS
[Termes IGN] système de référence local
[Termes IGN] Taïwan
[Termes IGN] tectonique des plaques
[Termes IGN] variation temporelleRésumé : (auteur) Although a dynamic or semi-dynamic datum has been adopted in some countries, it remains a challenge if a long-term stable datum is to be established in a tectonic active area. This study presents an approach to realistically reflect the time dependent behaviors of ground reference points while maintaining the long-term stability of a datum. An adaptive approach coupled with the Euler motion model is proposed for dividing an area into blocks. A least-squares collocation is then applied for modeling the residual velocities in each block. A case study using the data from 375 continuously operated GNSS stations in Taiwan is presented. It is illustrated that the complex surface kinematics in this region can be divided into three blocks. Significant reductions up to 64% of residual velocities were obtained. This shows that a stable datum can be established in a region with active and complicated surface kinematics by implementing the proposed. Numéro de notice : A2022-658 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1949194 Date de publication en ligne : 12/07/2021 En ligne : https://doi.org/10.1080/00396265.2021.1949194 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101509
in Survey review > vol 54 n° 386 (September 2022) . - pp 404 - 419[article]Assessing road accidents in spatial context via statistical and non-statistical approaches to detect road accident hotspot using GIS / Yegane Khosravi in Geodetski vestnik, vol 66 n° 3 (September - November 2022)PermalinkA boundary-based ground-point filtering method for photogrammetric point-cloud data / Seyed Mohammad Ayazi in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)PermalinkDesign and construction of a colourblind-friendly Surabaya city angkot route map prototype / Arzakhy Indhira Pramesti in Cartographica, vol 57 n° 3 (September 2022)PermalinkDiscontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)PermalinkExploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)PermalinkA high-resolution gravimetric geoid model for Kingdom of Saudi Arabia / Ahmed Zaki in Survey review, vol 54 n° 386 (September 2022)PermalinkHistorical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine / Luis Carrasco in ISPRS Journal of photogrammetry and remote sensing, vol 191 (September 2022)PermalinkImpact assessment of the seasonal hydrological loading on geodetic movement and seismicity in Nepal Himalaya using GRACE and GNSS measurements / Devendra Shashikant Nagale in Geodesy and Geodynamics, vol 13 n° 5 (September 2022)PermalinkMapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)PermalinkPoint-of-interest detection from Weibo data for map updating / Xue Yang in Transactions in GIS, vol 26 n° 6 (September 2022)Permalink