Interval prediction of TN based on the bidirectional long short-term memory- residual block-bayesian optimization model Article Swipe
Hanzhi Zhang
,
Guoqiang Niu
,
Qihang Huang
,
Xiaoyong Li
,
Mi Lin
,
Xiaohui Yi
,
Mingzhi Huang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7147232/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7147232/v1
Based on multi-station water quality data in Guangzhou section of the Pearl River Basin, a bidirectional long short-term memory - residual block-Bayesian optimization model (Bidirectional long short-term memory - residual block-Bayesian optimization model) is designed by combining BI-LSTM, residual network and Bayesian optimization. The results show that compared with the reference model, the model converges faster and the prediction accuracy is higher. To further investigate the impact of socioeconomic and land use factors on water quality, a random forest algorithm is employed to quantify the relative importance of land use composition and landscape pattern indices in influencing TN concentrations. The results reveal that variables such as land use intensity, landscape fragmentation, and specific land cover types substantially affect TN levels, indicating a strong correlation between anthropogenic activities and nitrogen pollution. This integrated modeling approach not only improves prediction accuracy but also provides important insights into the spatiotemporal mechanisms underlying water quality variation. The findings offer valuable support for data-driven decision-making in watershed management and targeted pollution mitigation strategies in rapidly urbanizing catchments.
Related Topics
Concepts
Residual
Term (time)
Interval (graph theory)
Block (permutation group theory)
Bayesian probability
Bayesian optimization
Computer science
Long short term memory
Credible interval
Algorithm
Artificial intelligence
Mathematics
Geometry
Combinatorics
Physics
Quantum mechanics
Artificial neural network
Recurrent neural network
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.21203/rs.3.rs-7147232/v1
- OA Status
- gold
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412898181
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412898181Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-7147232/v1Digital Object Identifier
- Title
-
Interval prediction of TN based on the bidirectional long short-term memory- residual block-bayesian optimization modelWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-29Full publication date if available
- Authors
-
Hanzhi Zhang, Guoqiang Niu, Qihang Huang, Xiaoyong Li, Mi Lin, Xiaohui Yi, Mingzhi HuangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-7147232/v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.21203/rs.3.rs-7147232/v1Direct OA link when available
- Concepts
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Residual, Term (time), Interval (graph theory), Block (permutation group theory), Bayesian probability, Bayesian optimization, Computer science, Long short term memory, Credible interval, Algorithm, Artificial intelligence, Mathematics, Geometry, Combinatorics, Physics, Quantum mechanics, Artificial neural network, Recurrent neural networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4387339229, https://openalex.org/W4283367183, https://openalex.org/W4306402945, https://openalex.org/W4362733027, https://openalex.org/W4312105465, https://openalex.org/W3149839747, https://openalex.org/W4220672066, https://openalex.org/W2608421173, https://openalex.org/W4244555221, https://openalex.org/W4286492427, https://openalex.org/W2936399818, https://openalex.org/W4323309891, https://openalex.org/W4210476605, https://openalex.org/W2972689348, https://openalex.org/W2930669685, https://openalex.org/W4386018749, https://openalex.org/W2024695206, https://openalex.org/W4387578347, https://openalex.org/W2970835038, https://openalex.org/W4387802347, https://openalex.org/W3163234680, https://openalex.org/W3198443798, https://openalex.org/W1981804036, https://openalex.org/W4366418523, https://openalex.org/W3159114776, https://openalex.org/W4390880644, https://openalex.org/W2498789492, https://openalex.org/W4310178189 |
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