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Approaches, Advances and Applications in Sustainable Development of Smart Cities

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ISBN: 9783039280124 / 9783039280131 Year: Pages: 308 DOI: 10.3390/books978-3-03928-013-1 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Economics
Added to DOAB on : 2020-06-09 16:38:57
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Abstract

This book aims to contribute to the conceptual and practical knowledge pools in order to improve the research and practice on the sustainable development of smart cities by bringing an informed understanding of the subject to scholars, policymakers, and practitioners. This book seeks articles offering insights into the sustainable development of smart cities by providing in-depth conceptual analyses and detailed case study descriptions and empirical investigations. This way, the book will form a repository of relevant information, material, and knowledge to support research, policymaking, practice, and transferability of experiences to address aforementioned challenges. The scope of the book includes the following broad areas, with a particular focus on the approaches, advances, and applications in the sustainable development of smart cities: • Theoretical underpinnings and analytical and policy frameworks; • Methodological approaches for the evaluation of smart and sustainable cities; • Technological developments in the techno-enviro nexus; • Global best practice smart city case investigations and reports; • Geo-design and applications concerning desired urban outcomes; • Prospects, implications, and impacts concerning the future of smart and sustainable cities.

Keywords

tourist island --- innovation hub --- knowledge-based urban development --- knowledge and innovation economy --- smart city --- urban branding --- urban policy --- economic resilience --- Florianópolis --- Brazil --- city branding --- sustainable urban development --- rentier state --- Qatar --- emirates --- smart cities --- mobility --- visioning --- policy --- energy budget --- land cover ratio --- sensible heat flux --- heat mitigation --- thermal environment improvement --- sustainability --- in-situ validation --- spatial typification by heat flux --- smart cities --- commons --- digital commons --- governance --- e-government --- smart governance --- new public service --- Brazil --- smart cities --- smart display --- smart placemaking --- human–computer interaction --- user characteristics --- media façade --- intuitive interaction --- living-lab --- optimal cities --- energy autonomy --- low-carbon resources --- multi-energy networks --- parametric optimisation --- CO2 networks --- drinking water networks --- reliability --- economic cost --- model predictive control --- linear parameter varying --- smart city --- multi-agent systems --- gamification --- photovoltaics --- renewable energy systems --- spatial databases --- climate change --- climate emergency --- climate crisis --- global warming --- sustainable urban development --- sustainable development goals --- smart cities --- disasters --- urban health --- urban policy --- smart cities --- Shenzhen --- Chinese cities --- latecomer’s advantage --- sustainability --- smart city --- sustainable smart city --- smart infrastructure --- smart urban technology --- smart governance --- sustainable city --- sustainable urban development --- knowledge-based urban development --- climate change --- urban informatics --- urban policy

Flood Forecasting Using Machine Learning Methods

Authors: --- ---
ISBN: 9783038975489 Year: Pages: 376 DOI: 10.3390/books978-3-03897-549-6 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-03-08 11:42:05
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Abstract

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Water

Keywords

data scarce basins --- runoff series --- data forward prediction --- ensemble empirical mode decomposition (EEMD) --- stopping criteria --- method of tracking energy differences (MTED) --- deep learning --- convolutional neural networks --- superpixel --- urban water bodies --- high-resolution remote-sensing images --- monthly streamflow forecasting --- artificial neural network --- ensemble technique --- phase space reconstruction --- empirical wavelet transform --- hybrid neural network --- flood forecasting --- self-organizing map --- bat algorithm --- particle swarm optimization --- flood routing --- Muskingum model --- machine learning methods --- St. Venant equations --- rating curve method --- nonlinear Muskingum model --- hydrograph predictions --- flood routing --- Muskingum model --- hydrologic models --- improved bat algorithm --- Wilson flood --- Karahan flood --- flood susceptibility modeling --- ANFIS --- cultural algorithm --- bees algorithm --- invasive weed optimization --- Haraz watershed --- ANN-based models --- flood inundation map --- self-organizing map (SOM) --- recurrent nonlinear autoregressive with exogenous inputs (RNARX) --- ensemble technique --- artificial neural networks --- uncertainty --- streamflow predictions --- sensitivity --- flood forecasting --- extreme learning machine (ELM) --- backtracking search optimization algorithm (BSA) --- the upper Yangtze River --- deep learning --- LSTM network --- water level forecast --- the Three Gorges Dam --- Dongting Lake --- Muskingum model --- wolf pack algorithm --- parameters --- optimization --- flood routing --- flash-flood --- precipitation-runoff --- forecasting --- lag analysis --- random forest --- machine learning --- flood prediction --- flood forecasting --- hydrologic model --- rainfall–runoff, hybrid & --- ensemble machine learning --- artificial neural network --- support vector machine --- natural hazards & --- disasters --- adaptive neuro-fuzzy inference system (ANFIS) --- decision tree --- survey --- classification and regression trees (CART), data science --- big data --- artificial intelligence --- soft computing --- extreme event management --- time series prediction --- LSTM --- rainfall-runoff --- flood events --- flood forecasting --- data assimilation --- particle filter algorithm --- micro-model --- Lower Yellow River --- ANN --- hydrometeorology --- flood forecasting --- real-time --- postprocessing --- machine learning --- early flood warning systems --- hydroinformatics --- database --- flood forecast --- Google Maps

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