Multi-Indicator Water Quality Prediction Using Multimodal Bottleneck Fusion and ITransformer with Attention

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

Water quality prediction methods forecast the future short or long-term trends of its changes, providing proactive advice for water pollution prevention and control. Existing water quality prediction methods only consider the historical data of single-type or multi-type water quality. However, meteorology and other factors also have a significant impact on water quality indicators. Therefore, only considering the historical data of water quality is not feasible. Unlike existing studies, this work proposes a hybrid water quality prediction model called CMI to solve the above problem. Before prediction, CMI incorporates a multimodal fusion mechanism of water quality time series and remote sensing images of meteorological rainfall. Moreover, CMI integrates the model of ConvNeXt V2 and a multimodal bottleneck transformer to extract image features for fusing the time series and images. Furthermore, it utilizes an emerging model of iTransformer to realize prediction with the fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMI outperforms other state-of-the-art fusion algorithms, and the water quality prediction accuracy with fused meteorological data is 13% higher on average than that with only water quality time series.

Identifier

85217831064 (Scopus)

ISBN

[9781665410205]

Publication Title

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

External Full Text Location

https://doi.org/10.1109/SMC54092.2024.10831495

ISSN

1062922X

First Page

2367

Last Page

2372

Grant

62073005

Fund Ref

National Natural Science Foundation of China

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