5,267 research outputs found
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
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
Optimal interpolation of satellite and ground data for irradiance nowcasting at city scales
We use a Bayesian method, optimal interpolation, to improve satellite derived irradiance estimates at city-scales using ground sensor data. Optimal interpolation requires error covariances in the satellite estimates and ground data, which define how information from the sensor locations is distributed across a large area. We describe three methods to choose such covariances, including a covariance parameterization that depends on the relative cloudiness between locations. Results are computed with ground data from 22 sensors over a 75×80 km area centered on Tucson, AZ, using two satellite derived irradiance models. The improvements in standard error metrics for both satellite models indicate that our approach is applicable to additional satellite derived irradiance models. We also show that optimal interpolation can nearly eliminate mean bias error and improve the root mean squared error by 50%
The First Comparison Between Swarm-C Accelerometer-Derived Thermospheric Densities and Physical and Empirical Model Estimates
The first systematic comparison between Swarm-C accelerometer-derived
thermospheric density and both empirical and physics-based model results using
multiple model performance metrics is presented. This comparison is performed
at the satellite's high temporal 10-s resolution, which provides a meaningful
evaluation of the models' fidelity for orbit prediction and other space weather
forecasting applications. The comparison against the physical model is
influenced by the specification of the lower atmospheric forcing, the
high-latitude ionospheric plasma convection, and solar activity. Some insights
into the model response to thermosphere-driving mechanisms are obtained through
a machine learning exercise. The results of this analysis show that the
short-timescale variations observed by Swarm-C during periods of high solar and
geomagnetic activity were better captured by the physics-based model than the
empirical models. It is concluded that Swarm-C data agree well with the
climatologies inherent within the models and are, therefore, a useful data set
for further model validation and scientific research.Comment: https://goo.gl/n4QvU
Deep Neural Network Regression and Sobol Sensitivity Analysis for Daily Solar Energy Prediction Given Weather Data
Solar energy forecasting plays an important role in both solar power plants and electricity grid. The effective forecasting is essential for efficient usage and management of the electricity grid, as well as for the solar energy trading. However, many of the existing models or algorithms are based on real physical laws, where tons of calculations, step-by-step modification, and many inputs are required. In this research, a novel deep Multi-layer Perceptron (MLP) based regression approach for predicting solar energy is proposed, in which the inputs are only ensemble weather forecasting data. The results demonstrate that our proposed deep Multi-layer Perceptron based regression approach for solar energy forecasting is efficient as well as accurate enough. A Sobol sensitivity analysis is performed over the trained model, determining the most important variables in the weather forecasting model data. The first-order and the total order Sobol sensitivity indices for quantifying feature importance, are calculated for each model input parameter. With using the process of feature removal, the result of Sobol sensitivity analysis is verified
Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review
The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features
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