MRIE: Enhanced Rainfall Intensity Estimation With Two-Stage Multimodal Deep Learning
Accurate rainfall intensity estimation is crucial for environmental monitoring and urban planning. However, the reliability of existing methods in practical applications is relatively low due to certain deficiencies in data processing and noise resistance. To address these issues, MRIE is proposed,...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11007152/ |
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| author | Jin Li Wentao Hu Zhigang Zhou |
| author_facet | Jin Li Wentao Hu Zhigang Zhou |
| author_sort | Jin Li |
| collection | DOAJ |
| description | Accurate rainfall intensity estimation is crucial for environmental monitoring and urban planning. However, the reliability of existing methods in practical applications is relatively low due to certain deficiencies in data processing and noise resistance. To address these issues, MRIE is proposed, a two-stage multimodal framework that integrates data from environmental sensors and audio recordings to improve estimation accuracy. MRIE effectively fuses complementary information from multiple sensor modalities and demonstrates strong robustness with the help of a cross-modal guidance module and advanced feature fusion techniques. Experimental results show that the MAE and RMSE of MRIE are reduced by 40.7% and 29.1% respectively, and the <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> is increased by 17.3%, compared with the best baseline. These research findings highlight the potential of MRIE as a powerful and reliable tool for sensor-based rainfall monitoring systems. |
| format | Article |
| id | doaj-art-120afb6bb1a346aaa2aac24bb3adfa5b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-120afb6bb1a346aaa2aac24bb3adfa5b2025-08-20T03:54:07ZengIEEEIEEE Access2169-35362025-01-0113886008860810.1109/ACCESS.2025.357144011007152MRIE: Enhanced Rainfall Intensity Estimation With Two-Stage Multimodal Deep LearningJin Li0https://orcid.org/0009-0004-0845-3001Wentao Hu1https://orcid.org/0009-0001-4335-4429Zhigang Zhou2https://orcid.org/0009-0003-2525-1906School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, ChinaSchool of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou, ChinaAccurate rainfall intensity estimation is crucial for environmental monitoring and urban planning. However, the reliability of existing methods in practical applications is relatively low due to certain deficiencies in data processing and noise resistance. To address these issues, MRIE is proposed, a two-stage multimodal framework that integrates data from environmental sensors and audio recordings to improve estimation accuracy. MRIE effectively fuses complementary information from multiple sensor modalities and demonstrates strong robustness with the help of a cross-modal guidance module and advanced feature fusion techniques. Experimental results show that the MAE and RMSE of MRIE are reduced by 40.7% and 29.1% respectively, and the <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> is increased by 17.3%, compared with the best baseline. These research findings highlight the potential of MRIE as a powerful and reliable tool for sensor-based rainfall monitoring systems.https://ieeexplore.ieee.org/document/11007152/Rainfall intensity estimationmultimodal sensorsnoise robustnessenvironmental monitoringdeep learning |
| spellingShingle | Jin Li Wentao Hu Zhigang Zhou MRIE: Enhanced Rainfall Intensity Estimation With Two-Stage Multimodal Deep Learning IEEE Access Rainfall intensity estimation multimodal sensors noise robustness environmental monitoring deep learning |
| title | MRIE: Enhanced Rainfall Intensity Estimation With Two-Stage Multimodal Deep Learning |
| title_full | MRIE: Enhanced Rainfall Intensity Estimation With Two-Stage Multimodal Deep Learning |
| title_fullStr | MRIE: Enhanced Rainfall Intensity Estimation With Two-Stage Multimodal Deep Learning |
| title_full_unstemmed | MRIE: Enhanced Rainfall Intensity Estimation With Two-Stage Multimodal Deep Learning |
| title_short | MRIE: Enhanced Rainfall Intensity Estimation With Two-Stage Multimodal Deep Learning |
| title_sort | mrie enhanced rainfall intensity estimation with two stage multimodal deep learning |
| topic | Rainfall intensity estimation multimodal sensors noise robustness environmental monitoring deep learning |
| url | https://ieeexplore.ieee.org/document/11007152/ |
| work_keys_str_mv | AT jinli mrieenhancedrainfallintensityestimationwithtwostagemultimodaldeeplearning AT wentaohu mrieenhancedrainfallintensityestimationwithtwostagemultimodaldeeplearning AT zhigangzhou mrieenhancedrainfallintensityestimationwithtwostagemultimodaldeeplearning |