Attention-based BiLSTM Model for Rainfall Prediction-Reference-Cited by-同舟云学术

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Attention-based BiLSTM Model for Rainfall Prediction

Published:2023-07-14 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 13th International Conference on Electronics Information and Emergency Communication (ICEIEC)
language:
Short-container-title:

Author:

Yu Hao¹,Zhang Jian¹,Guo Yuenan²,Qu Shunke²,Gong Mingchen¹,Zeng Xuanyu³,Chen Hao³,He Tianyuan³,Xiao Kejiang³

Affiliation:

1. Electric Power Research Institute Heilongjiang Electric Power Company Limited,Harbin,Heilongjiang Province,China,150030

2. Heilongjiang Electric Power Company Limited,Harbin,Heilongjiang Province,China,150090

3. China Normal University,Department of Artificial Intelligence Education Central,Wuhan,Hubei Province,China,430079

Funder

State Grid Corporation of China Science and Technology

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10199110/10199203/10200962.pdf?arnumber=10200962

Reference14 articles.

1. An image is worth 16×16 words: Transformers for image recognition at scale;alexey;ArXiv Preprint,2010

2. PFST-LSTM: A spatiotemporal LSTM model with pseudoflow prediction for precipitation nowcasting;luo;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020

3. DeepFlow: Large displacement optical flow with deep matching;philippe;Proceedings of the IEEE International Conference on Computer Vision,2013

4. Deep learning for precipitation nowcasting: A benchmark and a new model;shi;Advances in neural information processing systems,2017

5. Deep-Flow-GAN;zhang;Journal of Artificial Intelligence Research,2019

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