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Wavelet gated multi...
Wavelet gated multiformer for groundwater time series forecasting
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- Serravalle Reis Rodrigues, Vitor Hugo (författare)
- Geol Survey Brazil SGB, Brazil,Geological Survey of Brazil
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- de Melo Barros Junior, Paulo Roberto (författare)
- Petr Brasileiro SA, Brazil,Petrobras, Brazil
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- dos Santos Marinho, Euler Bentes (författare)
- Univ Fed Bahia, Brazil,Federal University of Bahia, Brazil
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- Lima de Jesus Silva, Jose Luis (författare)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten,ReAL
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(creator_code:org_t)
- Nature Publishing Group : NATURE PORTFOLIO, 2023
- 2023
- Engelska.
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Ingår i: Scientific Reports. - Nature Publishing Group : NATURE PORTFOLIO. - 2045-2322. ; 13
- Relaterad länk:
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https://doi.org/10.1...
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
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- Developing accurate models for groundwater control is paramount for planning and managing life-sustaining resources (water) from aquifer reservoirs. Significant progress has been made toward designing and employing deep-forecasting models to tackle the challenge of multivariate time-series forecasting. However, most models were initially taught only to optimize natural language processing and computer vision tasks. We propose the Wavelet Gated Multiformer, which combines the strength of a vanilla Transformer with the Wavelet Crossformer that employs inner wavelet cross-correlation blocks. The self-attention mechanism (Transformer) computes the relationship between inner time-series points, while the cross-correlation finds trending periodicity patterns. The multi-headed encoder is channeled through a mixing gate (linear combination) of sub-encoders (Transformer and Wavelet Crossformer) that output trending signatures to the decoder. This process improved the model’s predictive capabilities, reducing Mean Absolute Error by 31.26 % compared to the second-best performing transformer-like models evaluated. We have also used the Multifractal Detrended Cross-Correlation Heatmaps (MF-DCCHM) to extract cyclical trends from pairs of stations across multifractal regimes by denoising the pair of signals with Daubechies wavelets. Our dataset was obtained from a network of eight wells for groundwater monitoring in Brazilian aquifers, six rainfall stations, eleven river flow stations, and three weather stations with atmospheric pressure, temperature, and humidity sensors.
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