MMSTP: Multi-modal Spatiotemporal Feature Fusion Network for Precipitation Prediction-Reference-Cited by-同舟云学术

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MMSTP: Multi-modal Spatiotemporal Feature Fusion Network for Precipitation Prediction

Published:2023-06-23 Issue: Volume: Page:
ISSN:
Container-title:2023 6th International Symposium on Autonomous Systems (ISAS)
language:
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Author:

Zhang Tianbao¹,Wang Hongbin²,Niu Dan³,Shi Chunlei¹,Chen Xisong³,Jin Yulong⁴

Affiliation:

1. Southeast University-Monash University Joint Graduate School(Suzhou) Southeast University,Nanjing,China

2. Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric Sciences,Nanjing,China

3. Southeast University, Key Laboratory of Measurement and Control of CSE,School of Automation,Nanjing,China

4. ZC Rubber(Jiande) Co.Ltd,Hangzhou,China

Funder

China Meteorological Administration

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10164121/10164123/10164452.pdf?arnumber=10164452

Reference25 articles.

1. Contextual Sa-Attention Convolutional LSTM for Precipitation Nowcasting: A Spatiotemporal Sequence Forecasting View

2. Rainfall Prediction using Machine Learning & Deep Learning Techniques

3. Convolutional LSTM network: A machine learning approach forprecipitation nowcasting;shi;In Proceedings of the 28th International Conference on Neural Information Processing Systems (NeurIPS),2015

4. Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting

5. Use of NWP for nowcasting convective precipitation: Recent progress and challenges;juanzhen;Bulletin of the American Meteorological Society,2014

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