Rainfall-runoff prediction based on multi-modal data fusion

Shuai Wu; Xiaoli Li

doi:10.1117/12.3011460

7 December 2023 Rainfall-runoff prediction based on multi-modal data fusion

Shuai Wu, Xiaoli Li

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129411B (2023) https://doi.org/10.1117/12.3011460
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Traditional rainfall-runoff prediction methods are mainly based on a single data source, such as only using meteorological observation to analyze and predict numerical data. However, this method is often affected by factors such as data missing and noise interference, which leads to low accuracy of prediction results. To solve this problem, paper proposes a rainfall-runoff prediction model based on multi-modal data fusion. The model uses image data as a new modal to forecast rainfall-runoff, and then uses Transformer and CNN to extract important information of digital data and image data. Since image data can obtain more information about vegetation cover area, in this model, vegetation features are used as new features to fuse with other feature information to improve the accuracy of prediction results. The experimental results show that proposed method achieves good results in single-day average runoff prediction in terms of performance and accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuai Wu and Xiaoli Li "Rainfall-runoff prediction based on multi-modal data fusion", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129411B (7 December 2023); https://doi.org/10.1117/12.3011460

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KEYWORDS

Data modeling

Rain

Atmospheric modeling

Feature extraction

Feature fusion

Image fusion

Data fusion

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