AIMC Topic: Weather

Clear Filters Showing 51 to 60 of 126 articles

Deep learning for twelve hour precipitation forecasts.

Nature communications
Existing weather forecasting models are based on physics and use supercomputers to evolve the atmosphere into the future. Better physics-based forecasts require improved atmospheric models, which can be difficult to discover and develop, or increasin...

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning.

Sensors (Basel, Switzerland)
Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and suscept...

Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods.

Sensors (Basel, Switzerland)
Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such...

Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data.

Scientific reports
Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised mach...

ECMWF short-term prediction accuracy improvement by deep learning.

Scientific reports
This paper aims to describe and evaluate the proposed calibration model based on a neural network for post-processing of two essential meteorological parameters, namely near-surface air temperature (2 m) and 24 h accumulated precipitation. The main i...

Weather Classification by Utilizing Synthetic Data.

Sensors (Basel, Switzerland)
Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing location...

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources.

Sensors (Basel, Switzerland)
Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based app...

A Graph Neural Network with Spatio-Temporal Attention for Multi-Sources Time Series Data: An Application to Frost Forecast.

Sensors (Basel, Switzerland)
Frost forecast is an important issue in climate research because of its economic impact on several industries. In this study, we propose GRAST-Frost, a graph neural network (GNN) with spatio-temporal architecture, which is used to predict minimum tem...

Using multiple linear regression and BP neural network to predict critical meteorological conditions of expressway bridge pavement icing.

PloS one
Icy bridge deck in winter has tremendous consequences for expressway traffic safety, which is closely related to the bridge pavement temperature. In this paper, the critical meteorological conditions of icy bridge deck were predicted by multiple line...

Robust Real-Time Traffic Surveillance with Deep Learning.

Computational intelligence and neuroscience
Real-time vehicle monitoring in highways, roads, and streets may provide useful data both for infrastructure planning and for traffic management in general. Even though it is a classic research area in computer vision, advances in neural networks for...