Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting
Ground-based cloud image features high-spatiotemporal resolution, presenting detailed local cloud structures and valuable weather information, which are crucial for meteorological forecasting. However, the inherent fuzziness and dynamism of ground-based clouds have hindered the development of effect...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/18 |
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| author | Sheng Li Min Wang Minghang Shi Jiafeng Wang Ran Cao |
| author_facet | Sheng Li Min Wang Minghang Shi Jiafeng Wang Ran Cao |
| author_sort | Sheng Li |
| collection | DOAJ |
| description | Ground-based cloud image features high-spatiotemporal resolution, presenting detailed local cloud structures and valuable weather information, which are crucial for meteorological forecasting. However, the inherent fuzziness and dynamism of ground-based clouds have hindered the development of effective prediction algorithms, resulting in low accuracy. This paper presents CloudPredRNN++, a novel method for predicting ground-based cloud dynamics, leveraging a deep spatiotemporal sequence prediction network enhanced with a self-attention mechanism. Initially, a Cascaded Causal LSTM (CCLSTM) with a dual-memory group decoupling structure is designed to enhance the representation of short-term cloud changes. Next, self-attention memory units are incorporated to capture the long-term dependencies and emphasize the non-stationary characteristics of cloud movements. These components are integrated into cloud dynamic feature mining units, which concurrently extract spatiotemporal features to strengthen unified spatiotemporal modeling. Finally, by embedding gradient highway units and adding skip connection, CloudPredRNN++ is constructed into a hierarchical recursive structure, mitigating the gradient vanishing and enhancing the uniform modeling of temporal–spatial features. Experiments on the sequence ground-based cloud dataset demonstrate that CloudPredRNN++ can predict the future cloud state more accurately and quickly. Compared with other spatiotemporal sequence prediction models, CloudPredRNN++ shows significant improvements in evaluation metrics, improving the accuracy of cloud dynamics forecasting and alleviating long-term dependency decay, thus confirming the effectiveness in ground-based cloud prediction tasks. |
| format | Article |
| id | doaj-art-f10b5d9644614051a7cafd237af4034b |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-f10b5d9644614051a7cafd237af4034b2025-01-10T13:19:58ZengMDPI AGRemote Sensing2072-42922024-12-011711810.3390/rs17010018Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics ForecastingSheng Li0Min Wang1Minghang Shi2Jiafeng Wang3Ran Cao4School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230009, ChinaGround-based cloud image features high-spatiotemporal resolution, presenting detailed local cloud structures and valuable weather information, which are crucial for meteorological forecasting. However, the inherent fuzziness and dynamism of ground-based clouds have hindered the development of effective prediction algorithms, resulting in low accuracy. This paper presents CloudPredRNN++, a novel method for predicting ground-based cloud dynamics, leveraging a deep spatiotemporal sequence prediction network enhanced with a self-attention mechanism. Initially, a Cascaded Causal LSTM (CCLSTM) with a dual-memory group decoupling structure is designed to enhance the representation of short-term cloud changes. Next, self-attention memory units are incorporated to capture the long-term dependencies and emphasize the non-stationary characteristics of cloud movements. These components are integrated into cloud dynamic feature mining units, which concurrently extract spatiotemporal features to strengthen unified spatiotemporal modeling. Finally, by embedding gradient highway units and adding skip connection, CloudPredRNN++ is constructed into a hierarchical recursive structure, mitigating the gradient vanishing and enhancing the uniform modeling of temporal–spatial features. Experiments on the sequence ground-based cloud dataset demonstrate that CloudPredRNN++ can predict the future cloud state more accurately and quickly. Compared with other spatiotemporal sequence prediction models, CloudPredRNN++ shows significant improvements in evaluation metrics, improving the accuracy of cloud dynamics forecasting and alleviating long-term dependency decay, thus confirming the effectiveness in ground-based cloud prediction tasks.https://www.mdpi.com/2072-4292/17/1/18ground-based cloud predictionspatiotemporal prediction networkrecurrent neural networkself-attention mechanism |
| spellingShingle | Sheng Li Min Wang Minghang Shi Jiafeng Wang Ran Cao Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting Remote Sensing ground-based cloud prediction spatiotemporal prediction network recurrent neural network self-attention mechanism |
| title | Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting |
| title_full | Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting |
| title_fullStr | Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting |
| title_full_unstemmed | Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting |
| title_short | Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting |
| title_sort | leveraging deep spatiotemporal sequence prediction network with self attention for ground based cloud dynamics forecasting |
| topic | ground-based cloud prediction spatiotemporal prediction network recurrent neural network self-attention mechanism |
| url | https://www.mdpi.com/2072-4292/17/1/18 |
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