Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism.
Accurate prediction of multi-dimensional water quality indicators is critical for sustainable water resource management, yet existing methods often fail to address the high-dimensional, nonlinear, and spatially correlated nature of data from heterogeneous IoT sensors. To overcome these limitations,...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326870 |
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| author | Li Bo Lv Junrui Luo Xuegang |
| author_facet | Li Bo Lv Junrui Luo Xuegang |
| author_sort | Li Bo |
| collection | DOAJ |
| description | Accurate prediction of multi-dimensional water quality indicators is critical for sustainable water resource management, yet existing methods often fail to address the high-dimensional, nonlinear, and spatially correlated nature of data from heterogeneous IoT sensors. To overcome these limitations, we propose TGMHA (Tensor Decomposition and Gated Neural Network with Multi-Head Self-Attention), a novel hybrid model that integrates three key innovations: 1) Tensor-based Feature Extraction: We combine Standard Delay Embedding Transformation (SDET) with Tucker tensor decomposition to reconstruct raw time series into low-rank tensor representations, capturing latent spatio-temporal patterns while suppressing sensor noise. 2) Multi-Head Self-Attention for Inter-Indicator Dependencies: A multi-head self-attention mechanism explicitly models complex inter-dependencies among diverse water quality indicators (e.g., pH, dissolved oxygen, conductivity) via parallel feature subspace learning. 3) Efficient Long-Term Dependency Modeling: An encoder-decoder architecture with gated recurrent units (GRUs), optimized by adaptive rank selection, ensures efficient modeling of long-term dependencies without compromising computational performance. By unifying these components into an end-to-end trainable system, TGMHA surpasses conventional approaches in handling complex water quality dynamics, particularly in scenarios with missing data and nonlinear interactions. Rigorous evaluation against six state-of-the-art benchmarks confirms TGMHA's superior capability, offering a robust and interpretable paradigm for multi-sensor fusion and water quality forecasting in environmental informatics. |
| format | Article |
| id | doaj-art-86a63e3013714a42b5fd8dea73ff7b61 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-86a63e3013714a42b5fd8dea73ff7b612025-08-20T02:40:24ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032687010.1371/journal.pone.0326870Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism.Li BoLv JunruiLuo XuegangAccurate prediction of multi-dimensional water quality indicators is critical for sustainable water resource management, yet existing methods often fail to address the high-dimensional, nonlinear, and spatially correlated nature of data from heterogeneous IoT sensors. To overcome these limitations, we propose TGMHA (Tensor Decomposition and Gated Neural Network with Multi-Head Self-Attention), a novel hybrid model that integrates three key innovations: 1) Tensor-based Feature Extraction: We combine Standard Delay Embedding Transformation (SDET) with Tucker tensor decomposition to reconstruct raw time series into low-rank tensor representations, capturing latent spatio-temporal patterns while suppressing sensor noise. 2) Multi-Head Self-Attention for Inter-Indicator Dependencies: A multi-head self-attention mechanism explicitly models complex inter-dependencies among diverse water quality indicators (e.g., pH, dissolved oxygen, conductivity) via parallel feature subspace learning. 3) Efficient Long-Term Dependency Modeling: An encoder-decoder architecture with gated recurrent units (GRUs), optimized by adaptive rank selection, ensures efficient modeling of long-term dependencies without compromising computational performance. By unifying these components into an end-to-end trainable system, TGMHA surpasses conventional approaches in handling complex water quality dynamics, particularly in scenarios with missing data and nonlinear interactions. Rigorous evaluation against six state-of-the-art benchmarks confirms TGMHA's superior capability, offering a robust and interpretable paradigm for multi-sensor fusion and water quality forecasting in environmental informatics.https://doi.org/10.1371/journal.pone.0326870 |
| spellingShingle | Li Bo Lv Junrui Luo Xuegang Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. PLoS ONE |
| title | Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. |
| title_full | Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. |
| title_fullStr | Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. |
| title_full_unstemmed | Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. |
| title_short | Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. |
| title_sort | multi dimensional water quality indicators forecasting from iot sensors a tensor decomposition and multi head self attention mechanism |
| url | https://doi.org/10.1371/journal.pone.0326870 |
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