102 research outputs found
Wind speed prediction using multidimensional convolutional neural networks
Accurate wind speed forecasting is of great importance for many economic,
business and management sectors. This paper introduces a new model based on
convolutional neural networks (CNNs) for wind speed prediction tasks. In
particular, we show that compared to classical CNN-based models, the proposed
model is able to better characterise the spatio-temporal evolution of the wind
data by learning the underlying complex input-output relationships from
multiple dimensions (views) of the input data. The proposed model exploits the
spatio-temporal multivariate multidimensional historical weather data for
learning new representations used for wind forecasting. We conduct experiments
on two real-life weather datasets. The datasets are measurements from cities in
Denmark and in the Netherlands. The proposed model is compared with traditional
2- and 3-dimensional CNN models, a 2D-CNN model with an attention layer and a
2D-CNN model equipped with upscaling and depthwise separable convolutions.Comment: 8 pages, 6 figure
Block method for numerical solution of fuzzy differential equations
In this paper the 2 point 2 step method for solving fuzzy initial value problem is proposed. This method at each step will estimate the solutions of the fuzzy initial value problem at two points simultaneously using variable step size. The stability of the proposed method is discussed. Examples are presented to illustrate the computational aspect
of the method
SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture
Weather forecasting is dominated by numerical weather prediction that tries
to model accurately the physical properties of the atmosphere. A downside of
numerical weather prediction is that it is lacking the ability for short-term
forecasts using the latest available information. By using a data-driven neural
network approach we show that it is possible to produce an accurate
precipitation nowcast. To this end, we propose SmaAt-UNet, an efficient
convolutional neural networks-based on the well known UNet architecture
equipped with attention modules and depthwise-separable convolutions. We
evaluate our approaches on a real-life datasets using precipitation maps from
the region of the Netherlands and binary images of cloud coverage of France.
The experimental results show that in terms of prediction performance, the
proposed model is comparable to other examined models while only using a
quarter of the trainable parameters.Comment: 9 pages, 4 figure
Deep Graph Convolutional Networks for Wind Speed Prediction
In this paper, we introduce a new model for wind speed prediction based on spatio-temporal graph convolutional networks. Here, weather stations are treated as nodes of a graph with a learnable adjacency matrix, which determines the strength of relations between the stations based on the historical weather data. The self-loop connection is added to the learnt adjacency matrix and its strength is controlled by additional learnable parameter. Experiments performed on real datasets collected from weather stations located in Denmark and the Netherlands show that our proposed model outperforms previously developed baseline models on the referenced datasets
Deep coastal sea elements forecasting using U-Net based models
The supply and demand of energy is influenced by meteorological conditions.
The relevance of accurate weather forecasts increases as the demand for
renewable energy sources increases. The energy providers and policy makers
require weather information to make informed choices and establish optimal
plans according to the operational objectives. Due to the recent development of
deep learning techniques applied to satellite imagery, weather forecasting that
uses remote sensing data has also been the subject of major progress. The
present paper investigates multiple steps ahead frame prediction for coastal
sea elements in the Netherlands using U-Net based architectures. Hourly data
from the Copernicus observation programme spanned over a period of 2 years has
been used to train the models and make the forecasting, including seasonal
predictions. We propose a variation of the U-Net architecture and further
extend this novel model using residual connections, parallel convolutions and
asymmetric convolutions in order to introduce three additional architectures.
In particular, we show that the architecture equipped with parallel and
asymmetric convolutions as well as skip connections outperforms the other three
discussed models.Comment: 12 pages, 11 figure
Broad-UNet: Multi-scale feature learning for nowcasting tasks
Weather nowcasting consists of predicting meteorological components in the short term at high spatial resolutions. Due to its influence in many human activities, accurate nowcasting has recently gained plenty of attention. In this paper, we treat the nowcasting problem as an image-to-image translation problem using satellite imagery. We introduce Broad-UNet, a novel architecture based on the core UNet model, to efficiently address this problem. In particular, the proposed Broad-UNet is equipped with asymmetric parallel convolutions as well as Atrous Spatial Pyramid Pooling (ASPP) module. In this way, the Broad-UNet model learns more complex patterns by combining multi-scale features while using fewer parameters than the core UNet model. The proposed model is applied on two different nowcasting tasks, i.e. precipitation maps and cloud cover nowcasting. The obtained numerical results show that the introduced Broad-UNet model performs more accurate predictions compared to the other examined architectures
GCN-FFNN: A two-stream deep model for learning solution to partial differential equations
This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) architecture and feed-forward neural networks (FFNN) for learning the solution of nonlinear partial differential equations (PDEs). The model aims at incorporating both graph and grid input representations using two streams corresponding to GCN and FFNN models, respectively. Each stream layer receives and processes its input representation. As opposed to FFNN which receives a grid-like structure, the GCN stream layer operates on graph input data where the neighborhood information is incorporated through the adjacency matrix of the graph. In this way, the proposed GCN-FFNN model learns from two types of input representations, i.e. grid and graph data, obtained via the discretization of the PDE domain. The GCN-FFNN model is trained in two phases. In the first phase, the model parameters of each stream are trained separately. Both streams employ the same error function to adjust their parameters by enforcing the models to satisfy the given PDE as well as its initial and boundary conditions on grid or graph collocation (training) data. In the second phase, the learned parameters of two-stream layers are frozen and their learned representation solutions are fed to fully connected layers whose parameters are learned using the previously used error function. The learned GCN-FFNN model is tested on test data located both inside and outside the PDE domain. The obtained numerical results demonstrate the applicability and efficiency of the proposed GCN-FFNN model over individual GCN and FFNN models on 1D-Burgers, 1D-Schrödinger, 2D-Burgers, and 2D-Schrödinger equations
SmaAt-UNet: Precipitation nowcasting using a small attention-UNet architecture
Weather forecasting is dominated by numerical weather prediction that tries to model accurately the physical properties of the atmosphere. A downside of numerical weather prediction is that it is lacking the ability for short-term forecasts using the latest available information. By using a data-driven neural network approach we show that it is possible to produce an accurate precipitation nowcast. To this end, we propose SmaAt-UNet, an efficient convolutional neural networks-based on the well known UNet architecture equipped with attention modules and depthwise-separable convolutions. We evaluate our approaches on a real-life datasets using precipitation maps from the region of the Netherlands and binary images of cloud coverage of France. The experimental results show that in terms of prediction performance, the proposed model is comparable to other examined models while only using a quarter of the trainable parameters
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