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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 descripteurs IGN] arsenic [Termes descripteurs IGN] base de données localisées [Termes descripteurs IGN] Caroline du Nord (Etats-Unis) [Termes descripteurs IGN] contamination [Termes descripteurs IGN] eau souterraine [Termes descripteurs IGN] interpolation spatiale [Termes descripteurs IGN] krigeage [Termes descripteurs IGN] pollution des eaux [Termes descripteurs IGN] prévention des risques [Termes descripteurs IGN] puits [Termes descripteurs IGN] santé [Termes descripteurs IGN] surveillance sanitaire [Termes descripteurs IGN] système d'aide à la décision [Termes descripteurs IGN] système d'information géographique [Termes descripteurs 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]

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 descripteurs IGN] algorithme d'apprentissage [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Caroline du Nord (Etats-Unis) [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] géostatistique [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] modèle empirique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs 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 descripteurs IGN] informatique en nuage [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] lever des détails [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] modélisation [Termes descripteurs IGN] Mozambique [Termes descripteurs IGN] synergiciel [Termes descripteurs IGN] système d'aide à la décision [Termes descripteurs 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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