| Issue |
E3S Web of Conf.
Volume 405, 2023
2023 International Conference on Sustainable Technologies in Civil and Environmental Engineering (ICSTCE 2023)
|
|
|---|---|---|
| Article Number | 04003 | |
| Number of page(s) | 9 | |
| Section | Sustainable Technologies in Construction & Environmental Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202340504003 | |
| Published online | 26 July 2023 | |
Radar Based Precipitation Nowcasting Prediction by Using Deep Learning Techniques
Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh.
1
shaikimmu27@gmail.com, anuradha_it@vrsiddhartha.ac.in, bharatratnala21@gmail.com
Nowcasting is an emerging area in meteorology that focuses on accurately anticipating the severity of short-term rainfall for a particular location. It is essential to many facets of society. Owing to its significance, researchers are experimenting to predict short term rainfall using neural network approaches. This study analyses proposes a novel method of merging Convolutional Neural Network and Long Short-Term Memory neural networks on a radar echo dataset. The model was tested against a synthetic moving mnist dataset before applying on actual radar image dataset. Given the previous radar images, the model could successfully find future image sequences and obtained an accuracy of more than 90%.
Key words: CNN / ConvLSTM / nowcasting / precipitation / radar images
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.