Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNN

10/24/2018
by   Maitreya Patel, et al.
0

Short-term rainfall forecasting, also known as precipitation nowcasting has become a potentially fundamental technology impacting significant real-world applications ranging from flight safety, rainstorm alerts to farm irrigation timings. Since weather forecasting involves identifying the underlying structure in a huge amount of data, deep-learning based precipitation nowcasting has intuitively outperformed the traditional linear extrapolation methods. Our research work intends to utilize the recent advances in deep learning to nowcasting, a multi-variable time series forecasting problem. Specifically, we leverage a bidirectional LSTM (Long Short-Term Memory) neural network architecture which remarkably captures the temporal features and long-term dependencies from historical data. To further our studies, we compare the bidirectional LSTM network with 1D CNN model to prove the capabilities of sequence models over feed-forward neural architectures in forecasting related problems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/07/2018

Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

Short-term traffic forecasting based on deep learning methods, especiall...
research
06/24/2023

Comparative Study of Predicting Stock Index Using Deep Learning Models

Time series forecasting has seen many methods attempted over the past fe...
research
10/28/2022

DELFI: Deep Mixture Models for Long-term Air Quality Forecasting in the Delhi National Capital Region

The identification and control of human factors in climate change is a r...
research
09/06/2022

Impact analysis of recovery cases due to COVID19 using LSTM deep learning model

The present world is badly affected by novel coronavirus (COVID-19). Usi...
research
12/18/2014

Learning Temporal Dependencies in Data Using a DBN-BLSTM

Since the advent of deep learning, it has been used to solve various pro...
research
06/10/2020

Entanglement-Embedded Recurrent Network Architecture: Tensorized Latent State Propagation and Chaos Forecasting

Chaotic time series forecasting has been far less understood despite its...
research
08/19/2017

Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction

Data analytics helps basketball teams to create tactics. However, manual...

Please sign up or login with your details

Forgot password? Click here to reset