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Statistical Analysis and Stochastic Modelling of Hydrological Extremes

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ISBN: 9783039216642 / 9783039216659 Year: Pages: 294 DOI: 10.3390/books978-3-03921-665-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Meteorology and Climatology
Added to DOAB on : 2019-12-09 16:10:12
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Hydrological extremes have become a major concern because of their devastating consequences and their increased risk as a result of climate change and the growing concentration of people and infrastructure in high-risk zones. The analysis of hydrological extremes is challenging due to their rarity and small sample size, and the interconnections between different types of extremes and becomes further complicated by the untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The complexity of analyzing hydrological extremes calls for robust statistical methods for the treatment of such events. This Special Issue is motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions. The papers of this Special Issue focus on six topics associated with hydrological extremes: Historical changes in hydrological extremes; Projected changes in hydrological extremes; Downscaling of hydrological extremes; Early warning and forecasting systems for drought and flood; Interconnections of hydrological extremes; Applicability of satellite data for hydrological studies.

Keywords

rainfall --- monsoon --- high resolution --- TRMM --- drought prediction --- APCC Multi-Model Ensemble --- seasonal climate forecast --- machine learning --- sparse monitoring network --- Fiji --- drought analysis --- ANN model --- drought indices --- meteorological drought --- SIAP --- SWSI --- hydrological drought --- discrete wavelet --- global warming --- statistical downscaling --- HBV model --- flow regime --- uncertainty --- reservoir inflow forecasting --- artificial neural network --- wavelet artificial neural network --- weighted mean analogue --- variation analogue --- streamflow --- artificial neural network --- simulation --- forecasting --- support vector machine --- evolutionary strategy --- heavy storm --- hyetograph --- temperature --- clausius-clapeyron scaling --- climate change --- the Cauca River --- climate variability --- ENSO --- extreme rainfall --- trends --- statistical downscaling --- random forest --- least square support vector regression --- extreme rainfall --- polynomial normal transform --- multivariate modeling --- sampling errors --- non-normality --- extreme rainfall analysis --- statistical analysis --- hydrological extremes --- stretched Gaussian distribution --- Hurst exponent --- INDC pledge --- precipitation --- extreme events --- extreme precipitation exposure --- non-stationary --- extreme value theory --- uncertainty --- flood regime --- flood management --- Kabul river basin --- Pakistan --- extreme events --- innovative methods --- downscaling --- forecasting --- compound events --- satellite data

Brain-Computer Interfaces for Human Augmentation

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ISBN: 9783039219063 / 9783039219070 Year: Pages: 128 DOI: 10.3390/books978-3-03921-907-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Psychology
Added to DOAB on : 2019-12-09 11:49:16
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The field of Brain–Computer Interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of faster and more reliable assistive technologies based on direct links between the brain and an external device. Novel applications of BCIs have also been proposed, especially in the area of human augmentation, i.e., enabling people to go beyond human limitations in sensory, cognitive and motor tasks. Brain-imaging techniques, such as electroencephalography, have been used to extract neural correlates of various brain processes and transform them, via machine learning, into commands for external devices. Brain stimulation technology has allowed to trigger the activation of specific brain areas to enhance the cognitive processes associated to the task at hand, hence improving performance. BCIs have therefore extended their scope from assistive technologies for people with disabilities to neuro-tools for human enhancement. This Special Issue aims at showing the recent advances in BCIs for human augmentation, highlighting new results on both traditional and novel applications. These include, but are not limited to, control of external devices, communication, cognitive enhancement, decision making and entertainment.

Refrigeration Systems and Applications 2019

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ISBN: 9783039219520 / 9783039219537 Year: Pages: 200 DOI: 10.3390/books978-3-03921-953-7 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-01-07 09:08:26
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The Special Issue “Refrigeration Systems and Applications” aims to encourage researchers to address the concerns associated with climate change and the sustainability of artificial cold production systems, and to further the transition to the more sustainable technologies and methodologies of tomorrow through theoretical, experimental, and review research on the different applications of refrigeration and associated topics.

Advances in Water Distribution Networks

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ISBN: 9783038975564 Year: Pages: 174 DOI: 10.3390/books978-3-03897-557-1 Language: eng
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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The Special Issue on Advances in Water Distribution Networks (WDNs) explores four important topics of research in the framework of WDNs, namely simulation and optimization modelling, topology and partitioning, water quality, and service effectiveness. With regard to the first topic, the following aspects are addressed: pressure-driven formulations, algorithms for the optimal location of control valves to minimize leakage, the benefits of water discharge prediction for the remote real time control of valves, and transients generated by pumps operating as turbines. In the context of the second topic, a topological taxonomy of WDNs is presented, and partitioning methods for the creation of district metered areas are compared. In relation to the third topic, the vulnerability to trihalomethane is assessed, and a statistical optimization model to minimize heavy metal releases is presented. Finally, the fourth topic focusses on the estimation of non-revenue water, including leakage and unauthorized consumption, and on the assessment of service under intermittent supply conditions.

Computational Intelligence in Photovoltaic Systems

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ISBN: 9783039210985 / 9783039210992 Year: Pages: 180 DOI: 10.3390/books978-3-03921-099-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 16:10:12
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Photovoltaics, among the different renewable energy sources (RES), has become more popular. In recent years, however, many research topics have arisen as a result of the problems that are constantly faced in smart-grid and microgrid operations, such as forecasting of the output of power plant production, storage sizing, modeling, and control optimization of photovoltaic systems. Computational intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques for solving problems such as modeling, identification, optimization, availability prediction, forecasting, sizing, and control of stand-alone, grid-connected, and hybrid photovoltaic systems. This Special Issue will investigate the most recent developments and research on solar power systems. This Special Issue “Computational Intelligence in Photovoltaic Systems” is highly recommended for readers with an interest in the various aspects of solar power systems, and includes 10 original research papers covering relevant progress in the following (non-exhaustive) fields: Forecasting techniques (deterministic, stochastic, etc.); DC/AC converter control and maximum power point tracking techniques; Sizing and optimization of photovoltaic system components; Photovoltaics modeling and parameter estimation; Maintenance and reliability modeling; Decision processes for grid operators.

Masonry Buildings: Research and Practice

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ISBN: 9783039213733 / 9783039213740 Year: Pages: 202 DOI: 10.3390/books978-3-03921-374-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Construction
Added to DOAB on : 2019-12-09 11:49:15
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Masonry is a construction material that has been used throughout the years as a structural or non-structural component in buildings. Masonry can be described as a composite material made up of different units and diverse types of arrangements, with or without mortar, that is used in many ancient public buildings, as well as with the latest technologies being applied in construction. Research in multiple relevant fields, as well as crossing structural with non-structural needs, is crucial for understanding the qualities of existent buildings and to develop new products and construction technologies. This book addresses and promotes the discussion related to the different topics addressing the use of masonry in the construction sciences and in practice, including theory and research, numerical approaches and technical applications in new works, and repair actions and interventions in the built environment, connecting theory and application across topics from academia to industry.

Groundwater Resources and Salt Water Intrusion in a Changing Environment

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ISBN: 9783039211975 / 9783039211982 Year: Pages: 176 DOI: 10.3390/books978-3-03921-198-2 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2019-12-09 11:49:16
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This Special Issue presents the work of 30 scientists from 11 countries. It confirms that the impacts of global change, resulting from both climate change and increasing anthropogenic pressure, are huge on worldwide coastal areas (and critically so on some islands in the Pacific Ocean), with highly negative effects on coastal groundwater resources, which are widely affected by seawater intrusion. Some improved research methods are proposed in the contributions: using innovative hydrogeological, geophysical, and geochemical monitoring; assessing impacts of the changing environment on the coastal groundwater resources in terms of quantity and quality; and using modelling, especially to improve management approaches. The scientific research needed to face these challenges must continue to be deployed by different approaches based on the monitoring, modelling and management of groundwater resources. Novel and more efficient methods must be developed to keep up with the accelerating pace of global change.

Flood Forecasting Using Machine Learning Methods

Authors: --- ---
ISBN: 9783038975489 Year: Pages: 376 DOI: 10.3390/books978-3-03897-549-6 Language: eng
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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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

Plug-in Hybrid Electric Vehicle (PHEV)

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ISBN: 9783039214532 / 9783039214549 Year: Pages: 230 DOI: 10.3390/books978-3-03921-454-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Climate change, urban air quality, and dependency on crude oil are important societal challenges. In the transportation sector especially, clean and energy efficient technologies must be developed. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) have gained a growing interest in the vehicle industry. Nowadays, the commercialization of EVs and PHEVs has been possible in different applications (i.e., light duty, medium duty, and heavy duty vehicles) thanks to the advances in energy storage systems, power electronics converters (including DC/DC converters, DC/AC inverters, and battery charging systems), electric machines, and energy efficient power flow control strategies. This book is based on the Special Issue of the journal Applied Sciences on “Plug-In Hybrid Electric Vehicles (PHEVs)”. This collection of research articles includes topics such as novel propulsion systems, emerging power electronics and their control algorithms, emerging electric machines and control techniques, energy storage systems, including BMS, and efficient energy management strategies for hybrid propulsion, vehicle-to-grid (V2G), vehicle-to-home (V2H), grid-to-vehicle (G2V) technologies, and wireless power transfer (WPT) systems.

Keywords

battery power --- convex optimization --- dynamic programming --- engine-on power --- plug-in hybrid electric vehicle --- simulated annealing --- electric vehicle --- open-end winding --- dual inverter --- voltage vector distribution --- power sharing --- energy management --- range-extender --- CO2 --- air quality --- mobility needs --- LCA --- Paris Agreement --- hybrid energy storage system --- lithium-ion battery --- lithium-ion capacitor --- lifetime model --- power distribution --- state of health estimation --- adaptive neuron-fuzzy inference system (ANFIS) --- group method of data handling (GMDH) --- artificial neural network (ANN) --- electric vehicles (EVs) --- capacity degradation --- lithium-ion battery --- time-delay input --- interleaved multiport converte --- multi-objective genetic algorithm --- hybrid electric vehicles --- losses model --- wide bandgap (WBG) technologies --- Energy Storage systems --- LCA --- Well-to-Wheel --- electric vehicle --- plug-in hybrid --- electricity mix --- consequential --- attributional --- marginal --- system modelling --- energy system --- meta-analysis --- parallel hybrid electric vehicle --- regenerative braking --- fuel consumption characteristics --- energy efficiency --- state of charge --- lithium polymer battery --- electric vehicle --- Plugin Hybrid electric vehicle --- Li-ion battery --- modelling --- measurements --- state of charge --- strong track filter --- modified one-state hysteresis model --- Li(Ni1/3Co1/3Mn1/3)O2 battery --- energy management strategy --- Markov decision process (MDP) --- plug-in hybrid electric vehicles (PHEVs) --- Q-learning (QL) --- reinforcement learning (RL) --- novel propulsion systems --- emerging power electronics --- including wide bandgap (WBG) technology --- emerging electric machines --- efficient energy management strategies for hybrid propulsion systems --- energy storage systems --- life-cycle assessment (LCA)

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

Authors: ---
ISBN: 9783039212156 / 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
Added to DOAB on : 2019-12-09 11:49:15
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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Keywords

landslide --- bagging ensemble --- Logistic Model Trees --- GIS --- Vietnam --- colorization --- random forest regression --- grayscale aerial image --- change detection --- gully erosion --- environmental variables --- data mining techniques --- SCAI --- GIS --- mapping --- single-class data descriptors --- materia medica resource --- Panax notoginseng --- one-class classifiers --- geoherb --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- remote sensing image segmentation --- convolutional neural networks --- Gaofen-2 --- hybrid structure convolutional neural networks --- winter wheat spatial distribution --- classification-based learning --- real-time precise point positioning --- convergence time --- ionospheric delay constraints --- precise weighting --- landslide --- weights of evidence --- logistic regression --- random forest --- hybrid model --- traffic CO --- traffic CO prediction --- neural networks --- GIS --- land use/land cover (LULC) --- unmanned aerial vehicle --- texture --- gray-level co-occurrence matrix --- machine learning --- crop --- landslide susceptibility --- random forest --- boosted regression tree --- information gain --- landslide susceptibility map --- ALS point cloud --- multi-scale --- classification --- large scene --- coarse particle --- particulate matter 10 (PM10) --- landsat image --- machine learning --- support vector machine --- high-resolution --- optical remote sensing --- object detection --- deep learning --- transfer learning --- land subsidence --- Bayes net --- naïve Bayes --- logistic --- multilayer perceptron --- logit boost --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- leaf area index (LAI) --- machine learning --- Sentinel-2 --- sensitivity analysis --- training sample size --- spectral bands --- spatial sparse recovery --- constrained spatial smoothing --- spatial spline regression --- alternating direction method of multipliers --- landslide prediction --- machine learning --- neural networks --- model switching --- spatial predictive models --- predictive accuracy --- model assessment --- variable selection --- feature selection --- model validation --- spatial predictions --- reproducible research --- Qaidam Basin --- remote sensing --- TRMM --- artificial neural network --- n/a

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