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A web-based spatial decision support system for monitoring the risk of water contamination in private wells / Yu Lan in Annals of GIS, vol 26 n° 3 (July 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 3 (July 2020) . - pp 293 - 309 Titre : A web-based spatial decision support system for monitoring the risk of water contamination in private wells Type de document : Article/Communication Auteurs : Yu Lan, Auteur ; Wenwu Tang, Auteur ; Samantha Dye, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 293 - 309 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] arsenic [Termes IGN] base de données localisées [Termes IGN] Caroline du Nord (Etats-Unis) [Termes IGN] contamination [Termes IGN] eau souterraine [Termes IGN] interpolation spatiale [Termes IGN] krigeage [Termes IGN] pollution des eaux [Termes IGN] prévention des risques [Termes IGN] puits [Termes IGN] santé [Termes IGN] surveillance sanitaire [Termes IGN] système d'aide à la décision [Termes IGN] système d'information géographique [Termes IGN] WebSIG Résumé : (auteur) Long-term exposure to contaminated water can cause health effects, such as cancer. Accurate spatial prediction of inorganic compounds (e.g. arsenic) and pathogens in groundwater is critical for water supply management. Ideally, environmental health agencies would have access to an early warning system to alert well owners of risks of such contamination. The estimation and dissemination of these risks can be facilitated by the combination of Geographic Information Systems and spatial analysis capabilities – i.e., spatial decision support system (SDSS). However, the use of SDSS, especially web-based SDSS, is rare for spatially explicit studies of drinking water quality of private wells. In this study, we introduce the interactive Well Water Risk Estimation(iWWRE), a web-based SDSS to facilitate the monitoring of water contamination in private wells across Gaston County, North Carolina (US). Our system implements geoprocessing web services and generates dynamic spatial analysis results based on a database of private wells. Environmental health scientists using our system can conduct fine-grained spatial interpolation on 1) a particular type of contaminant such as arsenic, 2) on various subsets through a temporal query. Visuals consist of an estimation map, cross validation information, Kriging variance and contour lines that delineate areas with maximum contaminant levels (MCL), as set by the US Environmental Protection Agency (EPA). Our web-based SDSS was developed jointly with environmental health specialists who found it particularly critical for the monitoring of local contamination trends, and a useful tool to reach out to private well users in highly elevated contaminated areas. Numéro de notice : A2020-583 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1798508 Date de publication en ligne : 30/07/2020 En ligne : https://doi.org/10.1080/19475683.2020.1798508 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95905 [article]

Big data computing for geospatial applications / Zhenlong Li (2020)  

Public Titre : Big data computing for geospatial applications Type de document : Monographie Auteurs : Zhenlong Li, Éditeur scientifique ; Wenwu Tang, Éditeur scientifique ; Qunying Huang, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 222 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03943-245-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse géovisuelle [Termes IGN] analyse spatio-temporelle [Termes IGN] cyberinfrastructure [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées [Termes IGN] données massives [Termes IGN] informatique en nuage [Termes IGN] métadonnées [Termes IGN] représentation géographique [Termes IGN] réseau sémantique Résumé : (éditeur) The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. Note de contenu : 1- Introduction to Big Data computing for geospatial applications 2- MapReduce-based D-ELT framework to address the challenges of geospatial Big Data 3- High-performance overlay analysis of massive geographic polygons that considers shape complexity in a cloud environment 4- Parallel cellular automata Markov model for land use change prediction over MapReduce framework 5- Terrain analysis in Google Earth Engine: A method adapted for high-gerformance global-scale analysis 6- Integrating geovisual analytics with machine learning for human mobility pattern discovery 7- Social media Big Data mining and spatio-temporal analysis on public emotions for disaster mitigation 8- A novel method of missing road generation in city blocks based on big mobile navigation trajectory data 9- A task-oriented knowledge base for geospatial problem-solving 10- Geographic knowledge graph (GeoKG): A formalized geographic knowledge representation 11- Advanced cyberinfrastructure to enable search of big climate datasets in THREDDS Numéro de notice : 28389 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03943-245-5 En ligne : https://doi.org/10.3390/books978-3-03943-245-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98688

Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing / Minrui Zheng in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 1-2 (January - February 2019) . - pp 314 - 345 Titre : Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing Type de document : Article/Communication Auteurs : Minrui Zheng, Auteur ; Wenwu Tang, Auteur ; Xiang Zhao, Auteur Année de publication : 2019 Article en page(s) : pp 314 - 345 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse spatiale [Termes IGN] apprentissage profond [Termes IGN] Caroline du Nord (Etats-Unis) [Termes IGN] données spatiotemporelles [Termes IGN] géostatistique [Termes IGN] méthode des moindres carrés [Termes IGN] modèle empirique [Termes IGN] modèle numérique de surface [Termes IGN] modélisation spatiale [Termes IGN] optimisation (mathématiques) [Termes IGN] régression linéaire [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information foncière Résumé : (auteur) Artificial neural networks (ANNs) have been extensively used for the spatially explicit modeling of complex geographic phenomena. However, because of the complexity of the computational process, there has been an inadequate investigation on the parameter configuration of neural networks. Most studies in the literature from GIScience rely on a trial-and-error approach to select the parameter setting for ANN-driven spatial models. Hyperparameter optimization provides support for selecting the optimal architectures of ANNs. Thus, in this study, we develop an automated hyperparameter selection approach to identify optimal neural networks for spatial modeling. Further, the use of hyperparameter optimization is challenging because hyperparameter space is often large and the associated computational demand is heavy. Therefore, we utilize high-performance computing to accelerate the model selection process. Furthermore, we involve spatial statistics approaches to improve the efficiency of hyperparameter optimization. The spatial model used in our case study is a land price evaluation model in Mecklenburg County, North Carolina, USA. Our results demonstrate that the automated selection approach improves the model-level performance compared with linear regression, and the high-performance computing and spatial statistics approaches are of great help for accelerating and enhancing the selection of optimal neural networks for spatial modeling. Numéro de notice : A2019-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1530355 Date de publication en ligne : 12/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1530355 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91689 [article]

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A cyber-enabled spatial decision support system to inventory mangroves in Mozambique: coupling scientific workflows and cloud computing / Wenwu Tang in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 907 - 938 Titre : A cyber-enabled spatial decision support system to inventory mangroves in Mozambique: coupling scientific workflows and cloud computing Type de document : Article/Communication Auteurs : Wenwu Tang, Auteur ; Wenpeng Feng, Auteur ; Meijuan Jia, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 907 - 938 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] informatique en nuage [Termes IGN] inventaire de la végétation [Termes IGN] lever des détails [Termes IGN] mangrove [Termes IGN] modélisation [Termes IGN] Mozambique [Termes IGN] synergiciel [Termes IGN] système d'aide à la décision [Termes IGN] Zambèze (fleuve) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Mangroves are an important terrestrial carbon reservoir with numerous ecosystem services. Yet, it is difficult to inventory mangroves because of their low accessibility. A sampling approach that produces accurate assessment while maximizing logistical integrity of inventory operation is often required. Spatial decision support systems (SDSSs) provide support for integrating such a sampling design of fieldwork with operational considerations and evaluation of alternative scenarios. However, this fieldwork design driven by SDSS is often computationally intensive and repetitive. In this study, we develop a cyber-enabled SDSS framework to facilitate the computationally challenging fieldwork design that requires the efficacious selection of base camps and plots for the inventory of mangroves. Our study area is the Zambezi River Delta, Mozambique. Cyber-enabled capabilities, including scientific workflows and cloud computing, are integrated with the SDSS. Scientific workflows enable the automation of data and modeling tasks in the SDSS. Cloud computing offers on-demand computational support for interoperation among stakeholders for collaborative scenario evaluation for the fieldwork design of mangrove inventory. Further, this framework allows for harnessing high-performance computing capabilities for accelerating the fieldwork design. The cyber-enabled framework provides significant merits in terms of effective coordination among science and logistical teams, assurance of meeting inventory objectives, and an objective basis to collectively and efficaciously evaluate alternative scenarios. Numéro de notice : A2017-237 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1250900 En ligne : http://dx.doi.org/10.1080/13658816.2016.1250900 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85171 [article]

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