Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Machine learning in tropical cyclone forecast modeling: A review
Tropical cyclones have always been a concern of meteorologists, and there are many
studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting …
studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting …
Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data
Deep learning techniques such as convolutional neural networks (CNNs) can potentially
provide powerful tools for classifying, identifying, and predicting patterns in climate and …
provide powerful tools for classifying, identifying, and predicting patterns in climate and …
Precipitation prediction using recurrent neural networks and long short-term memory
MA Priatna, EC Djamal - … Computing Electronics and Control), 2020 - telkomnika.uad.ac.id
Prediction of meteorological variables such as precipitation, temperature, wind speed, and
solar radiation is beneficial for human life. The variable observations data is available from …
solar radiation is beneficial for human life. The variable observations data is available from …
Monitoring tropical cyclone using multi-source data and deep learning: a review
Z Fan, Y **, Y Yue, S Fang, J Liu - International Journal of Image …, 2024 - Taylor & Francis
Tropical cyclones (TCs) are highly destructive weather systems, typically accompanied by
heavy rainfall, extreme winds and storm surges, significantly impacting residents' safety and …
heavy rainfall, extreme winds and storm surges, significantly impacting residents' safety and …
[PDF][PDF] Learning to Focus and Track Extreme Climate Events.
This paper tackles the task of extreme climate event tracking. It has unique challenges
compared to other visual object tracking problems, including a wider range of spatio …
compared to other visual object tracking problems, including a wider range of spatio …
Spatiotemporal Data Analysis: A Review of Techniques, Applications, and Emerging Challenges
In recent years, spatiotemporal data has continued to proliferate with the development of
data collecting technologies such as the Global Positioning System (GPS), the Internet of …
data collecting technologies such as the Global Positioning System (GPS), the Internet of …
A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns
Convolutional neural networks (CNNs) can potentially provide powerful tools for classifying
and identifying patterns in climate and environmental data. However, because of the …
and identifying patterns in climate and environmental data. However, because of the …
Theoretical and Applied Deep Learning for Turbulence
A Chattopadhyay - 2022 - search.proquest.com
While turbulence remains the oldest unsolved mystery in physics, recent efforts in building
high-resolution physics-based simulation models, availability of highquality observational …
high-resolution physics-based simulation models, availability of highquality observational …