8,345 research outputs found
Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm
Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/042
Air pollution forecasts: An overview
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies
Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System
Peer reviewedPublisher PD
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
A Review on Different Modeling Techniques
In this study, the importance of air temperature from different aspects (e.g.,
human and plant health, ecological and environmental processes, urban
planning, and modelling) is presented in detail, and the major factors
affecting air temperature in urban areas are introduced. Given the importance
of air temperature, and the necessity of developing high-resolution spatio-
temporal air-temperature maps, this paper categorizes the existing approaches
for air temperature estimation into three categories (interpolation,
regression and simulation approaches) and reviews them. This paper focuses on
high-resolution air temperature mapping in urban areas, which is difficult due
to strong spatio-temporal variations. Different air temperature mapping
approaches have been applied to an urban area (Berlin, Germany) and the
results are presented and discussed. This review paper presents the
advantages, limitations and shortcomings of each approach in its original
form. In addition, the feasibility of utilizing each approach for air
temperature modelling in urban areas was investigated. Studies into the
elimination of the limitations and shortcomings of each approach are
presented, and the potential of developed techniques to address each
limitation is discussed. Based upon previous studies and developments, the
interpolation, regression and coupled simulation techniques show potential for
spatio-temporal modelling of air temperature in urban areas. However, some of
the shortcomings and limitations for development of high-resolution spatio-
temporal maps in urban areas have not been properly addressed yet. Hence, some
further studies into the elimination of remaining limitations, and improvement
of current approaches to high-resolution spatio-temporal mapping of air
temperature, are introduced as future research opportunities
Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS
Being able to effectively identify clouds and monitor their evolution is one
important step toward more accurate quantitative precipitation estimation and
forecast. In this study, a new gradient-based cloud-image segmentation
technique is developed using tools from image processing techniques. This
method integrates morphological image gradient magnitudes to separable cloud
systems and patches boundaries. A varying scale-kernel is implemented to reduce
the sensitivity of image segmentation to noise and capture objects with various
finenesses of the edges in remote-sensing images. The proposed method is
flexible and extendable from single- to multi-spectral imagery. Case studies
were carried out to validate the algorithm by applying the proposed
segmentation algorithm to synthetic radiances for channels of the Geostationary
Operational Environmental Satellites (GOES-R) simulated by a high-resolution
weather prediction model. The proposed method compares favorably with the
existing cloud-patch-based segmentation technique implemented in the
PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using
Artificial Neural Network - Cloud Classification System) rainfall retrieval
algorithm. Evaluation of event-based images indicates that the proposed
algorithm has potential to improve rain detection and estimation skills with an
average of more than 45% gain comparing to the segmentation technique used in
PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to
98%
Remote Sensing
This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas
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