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

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

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The Special Issue “Assessment and Nonlinear Modeling of Wave, Tidal, and Wind Energy Converters and Turbines” contributes original research to stimulate the continuing progress of the offshore renewable energy (ORE) field, with a focus on state-of-the-art numerical approaches developed for the design and analysis of ORE devices. Particularly, this collection provides new methodologies, analytical/numerical tools, and theoretical methods that deal with engineering problems in the ORE field of wave, wind, and current structures. This Special Issue covers a wide range of multidisciplinary aspects, such as the 1) study of generalized interaction wake model systems with elm variation for offshore wind farms; 2) a flower pollination method based on global maximum power point tracking strategy for point-absorbing type wave energy converters; 3) performance optimization of a Kirsten–Boeing turbine using a metamodel based on neural networks coupled with CFD; 4) proposal of a novel semi-submersible floating wind turbine platform composed of inclined columns and multi-segmented mooring lines; 5) reduction of tower fatigue through blade back twist and active pitch-to-stall control strategy for a semi-submersible floating offshore wind turbine; 6) assessment of primary energy conversion of a closed-circuit OWC wave energy converter; 7) development and validation of a wave-to-wire model for two types of OWC wave energy converters; 8) assessment of a hydrokinetic energy converter based on vortex-induced angular oscillations of a cylinder; 9) application of wave-turbulence decomposition methods on a tidal energy site assessment; 10) parametric study for an oscillating water column wave energy conversion system installed on a breakwater; 11) optimal dimensions of a semisubmersible floating platform for a 10 MW wind turbine; 12) fatigue life assessment for power cables floating in offshore wind turbines.

off-shore wind farms (OSWFs) --- wake model --- wind turbine (WT) --- Extreme Learning Machine (ELM) --- wind power (WP) --- large-eddy simulation (LES) --- point-absorbing --- wave energy converter (WEC) --- maximum power point tracking (MPPT) --- flower pollination algorithm (FPA) --- power take-off (PTO) --- hill-climbing method --- Kirsten–Boeing --- vertical axis turbine --- optimization --- neural nets --- Tensorflow --- ANSYS CFX --- metamodeling --- FOWT --- multi-segmented mooring line --- inclined columns --- semi-submersible --- AFWT --- floating offshore wind turbine (FOWT) --- pitch-to-stall --- blade back twist --- tower fore–aft moments --- negative damping --- blade flapwise moment --- tower axial fatigue life --- wave energy --- oscillating water column --- tank testing --- valves --- air compressibility --- air turbine --- wave-to-wire model --- energy harnessing --- energy converter --- flow-induced oscillations --- vortex-induced vibration --- flow–structure interaction --- hydrodynamics --- vortex shedding --- cylinder wake --- tidal energy --- site assessment --- wave-current interaction --- turbulence --- integral length scales --- wave-turbulence decomposition --- OWC --- wave power converting system --- parametric study --- caisson breakwater application --- floating offshore wind turbines --- frequency domain model --- semisubmersible platform --- 10 MW wind turbines --- large floating platform --- platform optimization --- wind energy --- floating offshore wind turbine --- dynamic analysis --- fatigue life assessment --- flexible power cables --- Daguragu / Kalkaringi / Wave Hill (Central NT SE52-08)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The Special Issue “Assessment and Nonlinear Modeling of Wave, Tidal, and Wind Energy Converters and Turbines” contributes original research to stimulate the continuing progress of the offshore renewable energy (ORE) field, with a focus on state-of-the-art numerical approaches developed for the design and analysis of ORE devices. Particularly, this collection provides new methodologies, analytical/numerical tools, and theoretical methods that deal with engineering problems in the ORE field of wave, wind, and current structures. This Special Issue covers a wide range of multidisciplinary aspects, such as the 1) study of generalized interaction wake model systems with elm variation for offshore wind farms; 2) a flower pollination method based on global maximum power point tracking strategy for point-absorbing type wave energy converters; 3) performance optimization of a Kirsten–Boeing turbine using a metamodel based on neural networks coupled with CFD; 4) proposal of a novel semi-submersible floating wind turbine platform composed of inclined columns and multi-segmented mooring lines; 5) reduction of tower fatigue through blade back twist and active pitch-to-stall control strategy for a semi-submersible floating offshore wind turbine; 6) assessment of primary energy conversion of a closed-circuit OWC wave energy converter; 7) development and validation of a wave-to-wire model for two types of OWC wave energy converters; 8) assessment of a hydrokinetic energy converter based on vortex-induced angular oscillations of a cylinder; 9) application of wave-turbulence decomposition methods on a tidal energy site assessment; 10) parametric study for an oscillating water column wave energy conversion system installed on a breakwater; 11) optimal dimensions of a semisubmersible floating platform for a 10 MW wind turbine; 12) fatigue life assessment for power cables floating in offshore wind turbines.

History of engineering & technology --- off-shore wind farms (OSWFs) --- wake model --- wind turbine (WT) --- Extreme Learning Machine (ELM) --- wind power (WP) --- large-eddy simulation (LES) --- point-absorbing --- wave energy converter (WEC) --- maximum power point tracking (MPPT) --- flower pollination algorithm (FPA) --- power take-off (PTO) --- hill-climbing method --- Kirsten–Boeing --- vertical axis turbine --- optimization --- neural nets --- Tensorflow --- ANSYS CFX --- metamodeling --- FOWT --- multi-segmented mooring line --- inclined columns --- semi-submersible --- AFWT --- floating offshore wind turbine (FOWT) --- pitch-to-stall --- blade back twist --- tower fore–aft moments --- negative damping --- blade flapwise moment --- tower axial fatigue life --- wave energy --- oscillating water column --- tank testing --- valves --- air compressibility --- air turbine --- wave-to-wire model --- energy harnessing --- energy converter --- flow-induced oscillations --- vortex-induced vibration --- flow–structure interaction --- hydrodynamics --- vortex shedding --- cylinder wake --- tidal energy --- site assessment --- wave-current interaction --- turbulence --- integral length scales --- wave-turbulence decomposition --- OWC --- wave power converting system --- parametric study --- caisson breakwater application --- floating offshore wind turbines --- frequency domain model --- semisubmersible platform --- 10 MW wind turbines --- large floating platform --- platform optimization --- wind energy --- floating offshore wind turbine --- dynamic analysis --- fatigue life assessment --- flexible power cables --- Daguragu / Kalkaringi / Wave Hill (Central NT SE52-08)