T^2-Net: A Semi-supervised Deep Model for Turbulence Forecasting

10/26/2020
by   Denghui Zhang, et al.
7

Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional turbulence forecasting approaches heavily rely on painstakingly customized turbulence indexes, which are less effective in dynamic and complex weather conditions. The recent availability of high-resolution weather data and turbulence records allows more accurate forecasting of the turbulence in a data-driven way. However, it is a non-trivial task for developing a machine learning based turbulence forecasting system due to two challenges: (1) Complex spatio-temporal correlations, turbulence is caused by air movement with complex spatio-temporal patterns, (2) Label scarcity, very limited turbulence labels can be obtained. To this end, in this paper, we develop a unified semi-supervised framework, T^2-Net, to address the above challenges. Specifically, we first build an encoder-decoder paradigm based on the convolutional LSTM to model the spatio-temporal correlations. Then, to tackle the label scarcity problem, we propose a novel Dual Label Guessing method to take advantage of massive unlabeled turbulence data. It integrates complementary signals from the main Turbulence Forecasting task and the auxiliary Turbulence Detection task to generate pseudo-labels, which are dynamically utilized as additional training data. Finally, extensive experimental results on a real-world turbulence dataset validate the superiority of our method on turbulence forecasting.

READ FULL TEXT
research
02/01/2021

Spatio-temporal Weather Forecasting and Attention Mechanism on Convolutional LSTMs

Numerical weather forecasting on high-resolution physical models consume...
research
02/27/2022

Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast

Tropical cyclone (TC) is an extreme tropical weather system and its traj...
research
10/01/2021

SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series

Learning from Multivariate Time Series (MTS) has attracted widespread at...
research
06/24/2023

Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset

Air quality forecasting has garnered significant attention recently, wit...
research
05/07/2022

Deep learning for spatio-temporal forecasting – application to solar energy

This thesis tackles the subject of spatio-temporal forecasting with deep...
research
08/07/2021

What a million Indian farmers say?: A crowdsourcing-based method for pest surveillance

Many different technologies are used to detect pests in the crops, such ...

Please sign up or login with your details

Forgot password? Click here to reset