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Development of a GIS and model-based method for optimizing the selection of locations for drinking water extraction by means of riverbank filtration / Yan Zhou (2020)  

Public Titre : Development of a GIS and model-based method for optimizing the selection of locations for drinking water extraction by means of riverbank filtration Type de document : Thèse/HDR Auteurs : Yan Zhou, Auteur Editeur : Dresde [Allemagne] : Technische Universität Dresden Année de publication : 2020 Importance : 181 p. Format : 21 x 30 cm Note générale : bibliographie A dissertation in partial fulfillment of the requirements for the degree of Doctor rerum naturalium Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] acquisition de données [Termes IGN] assainissement [Termes IGN] Chine [Termes IGN] eau de surface [Termes IGN] eau potable [Termes IGN] géostatistique [Termes IGN] gestion de l'eau [Termes IGN] krigeage [Termes IGN] rive Résumé : (auteur) The lack of safe drinking water worldwide has drawn the attention of decision makers to riverbank filtration (RBF) for its many advantages in purifying surface water. This study provides an overview of the hydrogeologic, fluvial, and environmental influences on the performance of RBF systems and aims to develop a model for RBF site selection. Using multi-attribute utility theory (MAUT), this study structured the RBF siting problem and assessed a multiplicative utility function for the decision maker. In a case study, geostatistical methods were used to acquire the necessary data and geographic information systems (GIS) were used to screen sites suitable for RBF implementation. Those suitable sites were then evaluated and ranked using the multi-attribute utility model. The result showed that sites can be identified as most preferred among the selected suitable sites based on their expected utility values. This study definitively answers the question regarding the capability of MAUT in RBF site selection. Further studies are needed to verify the influences of the attributes on the performance of RBF systems. Note de contenu : 1- Introduction 2- Fundamentals and Literature Review 3- Developing a Multi-attribute Utility Model for RBF Site Selection 4- Case Study 5- Conclusions and Recommendations Numéro de notice : 28610 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis : Environmental Science : Technische Universität Dresden : 2020 DOI : sans En ligne : http://d-nb.info/1227833202/34 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99477

A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy / Yan Zhou in Transactions in GIS, Vol 23 n° 5 (October 2019)  

Public [article]  inTransactions in GIS > Vol 23 n° 5 (October 2019) . - pp 1125 - 1151 Titre : A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy Type de document : Article/Communication Auteurs : Yan Zhou, Auteur ; Yanxi Li, Auteur ; Qing Zhu, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1125 - 1151 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] analyse spatio-temporelle [Termes IGN] bicyclette [Termes IGN] données météorologiques [Termes IGN] gestion prévisionnelle [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] regroupement de données [Termes IGN] SOLAP [Termes IGN] trafic routier [Termes IGN] Washington D.C. Résumé : (auteur) Bike‐sharing systems have been widely used in major cities across the world. As bike borrowing and return at different stations in different periods are not balanced, the bikes in a bike‐sharing system need to be redistributed frequently to rebalance the system. Therefore, traffic flow forecasting of the bike‐sharing system is an important issue, as this is conducive to achieving rebalancing of the bike system. In this article, we present a new traffic flow prediction approach based on the temporal links in dynamic traffic flow networks. A station clustering algorithm is first introduced to cluster stations into groups. A temporal link prediction method based on the dynamic traffic flow network method (STW+M) is then proposed to predict the traffic flow between stations. In our method, the non‐negative tensor decomposition and time‐series analysis model capture the rich information (temporal variabilities, spatial characteristics, and weather information) of the across‐clusters transition. Then, a temporal link prediction strategy is used to forecast potential links and weights in the traffic flow network by investigating both the network structure and the results of tensor computations. In order to assess the methods proposed in this article, we have used the data of bike‐sharing systems in New York and Washington, DC to conduct bike traffic prediction and the experimental results have shown that our method produces the lowest root mean square error (RMSE) and mean square error (MSE). Compared to four prediction methods from the literature, our RMSE and MSE of the two datasets have been lowered by an average of 2.55 (Washington, DC) and 2.41 (New York) and 3.35 (Washington, DC) and 2.96 (New York), respectively. The results show that the proposed approach improves predictions of traffic flow. Numéro de notice : A2019-551 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12560 Date de publication en ligne : 03/07/2019 En ligne : https://doi.org/10.1111/tgis.12560 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94201 [article]