Centre de documentation
scientifique

GIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 6 (June 2022) . - n° 336 Titre : GIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data Type de document : Article/Communication Auteurs : Wanqin He, Auteur ; Sara Shirowzhan, Auteur ; Christopher Pettit, Auteur Année de publication : 2022 Article en page(s) : n° 336 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] brousse [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] coefficient de corrélation [Termes IGN] données météorologiques [Termes IGN] données spatiotemporelles [Termes IGN] humidité du sol [Termes IGN] incendie [Termes IGN] indice de végétation [Termes IGN] Nouvelle-Galles du Sud [Termes IGN] prévention des risques [Termes IGN] régression linéaire [Termes IGN] Spark [Termes IGN] système d'information géographique [Termes IGN] température de l'air Résumé : (auteur) The causes of bushfires are extremely complex, and their scale of burning and probability of occurrence are influenced by the interaction of a variety of factors such as meteorological factors, topography, human activity and vegetation type. An in-depth understanding of the combined mechanisms of factors affecting the occurrence and spread of bushfires is needed to support the development of effective fire prevention plans and fire suppression measures and aid planning for geographic, ecological maintenance and urban emergency management. This study aimed to explore how bushfires, meteorological variability and other natural factors have interacted over the past 40 years in NSW Australia and how these influencing factors synergistically drive bushfires. The CSIRO’s Spark toolkit has been used to simulate bushfire burning spread over 24 h. The study uses NSW wildfire data from 1981–2020, combined with meteorological factors (temperature, precipitation, wind speed), vegetation data (NDVI data, vegetation type) and topography (slope, soil moisture) data to analyse the relationship between bushfires and influencing factors quantitatively. Machine learning-random forest regression was then used to determine the differences in the influence of bushfire factors on the incidence and burn scale of bushfires. Finally, the data on each influence factor was imported into Spark, and the results of the random forest model were used to set different influence weights in Spark to visualise the spread of bushfires burning over 24 h in four hotspot regions of bushfire in NSW. Wind speed, air temperature and soil moisture were found to have the most significant influence on the spread of bushfires, with the combined contribution of these three factors exceeding 60%, determining the spread of bushfires and the scale of burning. Precipitation and vegetation showed a greater influence on the annual frequency of bushfires. In addition, burn simulations show that wind direction influences the main direction of fire spread, whereas the shape of the flame front is mainly due to the influence of land classification. Besides, the simulation results from Spark could predict the temporal and spatial spread of fire, which is a potential decision aid for fireproofing agencies. The results of this study can inform how fire agencies can better understand fire occurrence mechanisms and use bushfire prediction and simulation techniques to support both their operational (short-term) and strategic (long-term) fire management responses and policies. Numéro de notice : A2022-481 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11060336 Date de publication en ligne : 05/06/2022 En ligne : https://doi.org/10.3390/ijgi11060336 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100894 [article]

Smart cities and construction technologies / Kefeng Zhang (2020)  

Public Titre : Smart cities and construction technologies Type de document : Monographie Auteurs : Kefeng Zhang, Éditeur scientifique ; Sara Shirowzhan, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 206 p. ISBN/ISSN/EAN : 978-1-83880-398-8 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] impression 3D [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] réalité virtuelle [Termes IGN] système d'information géographique [Termes IGN] ville intelligente Résumé : (Editeur) This book includes nine chapters presenting the outcome of research projects relevant to building, cities, and construction. A description of a smart city and the journey from conventional to smart cities is discussed at the beginning of the book. Innovative case studies of underground cities and floating city bridges are presented in this book. BIM and GIS applications on different projects, and the concept of intelligent contract and virtual reality are discussed. Two concepts relevant to conventional buildings including private open spaces and place attachments are also included, and these topics can be upgraded in the future by smart technologies. Note de contenu : Section one - Smart City Studies 1. A Journey from Conventional Cities to Smart Cities / Aman Kumar and Jasvir Singh Rattan 2. Earthscraper: A Smart Solution for Developing Future Underground Cities / Faham Tahmasebinia, Kevin Yu, Jiachen Bao, George Chammoun, Edwin Chang, Samad Sepasgozar and Fernando Alonso Marroquin 3. Floating Cities Bridge in 2050 / Faham Tahmasebinia, Yutaka Tsumura, Baichuan Wang, Yang Wen, Cheng Bao, Samad Sepasgozar and Fernando Alonso-Marroquin 4. A GIS-Based Risk and Safety Analysis of Entrance Areas in Educational Buildings Based on Students’ Experience / Sara Shirowzhan, Laurence Kimmel, Mohammad Mojtahedi, Samad Sepasgozar and Jack Peacock 5. The Effect of Place Attachment on Educational Efficiency in Elementary Schools / Farhad Soheili, Reyhaneh Karimi, Behnaz Avazpour and Samad M.E. Sepasgozar 6. Effective Factors on Desirability of Private Open Spaces: A Case Study of Kuye Nasr Residential Buildings, Tehran / Reyhaneh Karimi, Behnaz Avazpour and Samad M.E. Sepasgozar Section two - Technology Applications in Construction 7. Automating the Chaos: Intelligent Construction Contracts / Alan McNamara 8. 5D BIM Applications in Quantity Surveying: Dynamo and 3D Printing Technologies / Anqi Shi, Sara Shirowzhan, Samad M.E. Sepasgozar and Alireza Kaboli 9. An Investigation of Virtual Reality Technology Adoption in the Construction Industry / Mohsen Ghobadi and Samad M.E. Sepasgozar Numéro de notice : 26516 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.86103 En ligne : http://doi.org/10.5772/intechopen.86103 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97303

Spatial big data, BIM and advanced GIS for smart transformation / Sara Shirowzhan (2020)  

Public Titre : Spatial big data, BIM and advanced GIS for smart transformation Type de document : Monographie Auteurs : Sara Shirowzhan, Éditeur scientifique ; Willie Tan, Éditeur scientifique ; Samad R.E. Sepasgozar, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 166 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-03936-031-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification barycentrique [Termes IGN] cycliste [Termes IGN] données massives [Termes IGN] modèle orienté agent [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] optimisation (mathématiques) [Termes IGN] planification urbaine [Termes IGN] réseau ferroviaire [Termes IGN] réseau routier [Termes IGN] secours d'urgence [Termes IGN] système d'information géographique [Termes IGN] téléphone intelligent [Termes IGN] trafic routier [Termes IGN] ville intelligente Résumé : (éditeur) This book covers a range of topics including selective technologies and algorithms that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This book also presents an agenda for future investigations to address the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities. Some of selected specific tools presented in this book are as a simulator for improving the smart parking practices by modelling drivers with activity plans, a bike optimization algorithm to increase the efficiency of bike stations, an agent-based model simulation of human mobility with the use of mobile phone datasets. In addition, this book describes the use of numerical methods to match the network demand and supply of bicycles, investigate the distribution of railways using different indicators, presents a novel algorithm of direction-aware continuous moving K-nearest neighbor queries in road networks, and presents an efficient staged evacuation planning algorithm for multi-exit buildings. Note de contenu : 1- Digital twin and cyberGIS for improving connectivity and measuring the impact of infrastructure construction planning in smart cities 2- An efficient staged evacuation planning algorithm applied to multi-exit buildings 3- A hybrid framework for high-performance modeling of three-dimensional pipe networks 4- Direction-aware continuous moving K-nearest-neighbor query in road networks 5- The distribution pattern of the railway network in China at the county level 6- Data-driven bicycle network analysis based on traditional counting methods and GPS traces from smartphone 7- An agent-based model simulation of human mobility based on mobile phone data: How commuting relates to congestion 8- Heuristic bike optimization algorithm to improve usage efficiency of the station-free bike sharing system in Shenzhen, China 9- An occupancy simulator for a smart parking system: Developmental design and experimental considerations Numéro de notice : 28440 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03936-031-4 En ligne : https://doi.org/10.3390/books978-3-03936-031-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98877