A groundwater level spatiotemporal prediction model based on graph convolutional networks with a long short-term memory Article Swipe
Lifang Wang
,
Zhengwen Jiang
,
Lei Song
,
Xi Yu
,
Shujun Yuan
,
Baoyi Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2166/hydro.2024.226
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2166/hydro.2024.226
The performance of regional groundwater level (GWL) prediction model hinges on understanding intricate spatiotemporal correlations among monitoring wells. In this study, a graph convolutional network (GCN) with a long short-term memory (LSTM) (GCN–LSTM) model is introduced for GWL prediction utilizing data from 16 wells located in the northeastern Xiangtan City, China. This model is designed to account for both the hybrid temporal dependencies and spatial autocorrelations among wells. It consists of two parts: the spatial part employs GCNs to extract spatial characteristics from a spatial self-similarity weight matrix and an attribute self-similarity weight matrix among wells; the temporal part utilizes a LSTM module to capture the temporal patterns of GWL sequences, along with monthly precipitation and temperature data. This model dynamically predicts changes in groundwater levels, achieving higher accuracy on average compared to single-well predictions using LSTM. By incorporating both temporal dependencies and spatial autocorrelations, the GCN–LSTM model demonstrated an average improvement in goodness-of-fit of approximately 11.21% over the LSTM-based model for individual wells. Its application holds significant reference value for the sustainable utilization and development of groundwater resources in Xiangtan City.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.2166/hydro.2024.226
- OA Status
- diamond
- Cited By
- 6
- References
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All OpenAlex metadata
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https://openalex.org/W4404060012Canonical identifier for this work in OpenAlex
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https://doi.org/10.2166/hydro.2024.226Digital Object Identifier
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A groundwater level spatiotemporal prediction model based on graph convolutional networks with a long short-term memoryWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-11-01Full publication date if available
- Authors
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Lifang Wang, Zhengwen Jiang, Lei Song, Xi Yu, Shujun Yuan, Baoyi ZhangList of authors in order
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https://doi.org/10.2166/hydro.2024.226Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.2166/hydro.2024.226Direct OA link when available
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Term (time), Computer science, Graph, Long short term memory, Artificial intelligence, Theoretical computer science, Artificial neural network, Recurrent neural network, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 4, 2024: 2Per-year citation counts (last 5 years)
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| referenced_works | https://openalex.org/W4392767783, https://openalex.org/W3130402964, https://openalex.org/W3177929715, https://openalex.org/W3210458411, https://openalex.org/W2905215511, https://openalex.org/W4389098134, https://openalex.org/W4391483848, https://openalex.org/W4388997335, https://openalex.org/W3181693116, https://openalex.org/W4387618981, https://openalex.org/W3040305819, https://openalex.org/W4394786040, https://openalex.org/W3217756247, https://openalex.org/W1656598679, https://openalex.org/W2035217709, https://openalex.org/W1501856433, https://openalex.org/W4283366726, https://openalex.org/W3175039957, https://openalex.org/W2064675550, https://openalex.org/W2128084896, https://openalex.org/W6850258509, https://openalex.org/W2807252330, https://openalex.org/W6726873649, https://openalex.org/W4385666331, https://openalex.org/W3159928446, https://openalex.org/W2136618539, https://openalex.org/W4310170663, https://openalex.org/W3124590480, https://openalex.org/W4210472829, https://openalex.org/W4206948839, https://openalex.org/W2735484631, https://openalex.org/W4387993242, https://openalex.org/W2907891425, https://openalex.org/W2129629964, https://openalex.org/W1498436455, https://openalex.org/W1553433319, https://openalex.org/W4220922322, https://openalex.org/W2623944707, https://openalex.org/W4389961073, https://openalex.org/W6674330103, https://openalex.org/W2067872494, https://openalex.org/W4385480733, https://openalex.org/W3088611441, https://openalex.org/W4386579478, https://openalex.org/W2150355110, https://openalex.org/W3149057642, https://openalex.org/W4214761414, https://openalex.org/W3151387141, https://openalex.org/W4220732246, https://openalex.org/W4312221092, https://openalex.org/W2109242206, https://openalex.org/W2756203131, https://openalex.org/W4302292814, https://openalex.org/W4283736899, https://openalex.org/W2964319113, https://openalex.org/W2910892140, https://openalex.org/W4388160037, https://openalex.org/W4388806427, https://openalex.org/W4401075967, https://openalex.org/W4391885303, https://openalex.org/W4389100744, https://openalex.org/W2979535451, https://openalex.org/W2901504064, https://openalex.org/W2146291222, https://openalex.org/W4292832893, https://openalex.org/W2964015378, https://openalex.org/W2095705004, https://openalex.org/W4321457816, https://openalex.org/W3103720336 |
| referenced_works_count | 69 |
| abstract_inverted_index.a | 22, 28, 84, 101 |
| abstract_inverted_index.16 | 43 |
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| abstract_inverted_index.In | 19 |
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| abstract_inverted_index.is | 35, 54 |
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| abstract_inverted_index.The | 1 |
| abstract_inverted_index.and | 64, 89, 116, 143, 175 |
| abstract_inverted_index.for | 37, 58, 162, 171 |
| abstract_inverted_index.the | 47, 60, 74, 97, 106, 146, 159, 172 |
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| abstract_inverted_index.LSTM | 102 |
| abstract_inverted_index.This | 52, 119 |
| abstract_inverted_index.both | 59, 140 |
| abstract_inverted_index.data | 41 |
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| abstract_inverted_index.study, | 21 |
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| abstract_inverted_index.self-similarity | 86, 92 |
| abstract_inverted_index.autocorrelations | 66 |
| abstract_inverted_index.autocorrelations, | 145 |
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| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |