Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model
Abstract This paper presents a hybrid prediction model, ECOA-BiTCN-BiLSTM, for predicting dew in cold areas. The model integrates BiTCN and BiLSTM neural networks to enhance performance. An enhanced Crayfish optimization algorithm (ECOA) with four mixed strategies was employed to optimize the model’...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-74097-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862449892229120 |
|---|---|
| author | Yi Zhang Pengtao Liu Yingying Xu Meng Zhang |
| author_facet | Yi Zhang Pengtao Liu Yingying Xu Meng Zhang |
| author_sort | Yi Zhang |
| collection | DOAJ |
| description | Abstract This paper presents a hybrid prediction model, ECOA-BiTCN-BiLSTM, for predicting dew in cold areas. The model integrates BiTCN and BiLSTM neural networks to enhance performance. An enhanced Crayfish optimization algorithm (ECOA) with four mixed strategies was employed to optimize the model’s hyperparameters and reduce the impact of arbitrary selection. The proposed ECOA-BiTCN-BiLSTM model was validated using dew data from farmland in a northeastern Chinese city. Comparative experiments were conducted against the BiTCN model, the BiLSTM model, the original BiTCN-BiLSTM model, and other models optimized with advanced swarm intelligence algorithms. The experimental results demonstrate that the proposed model achieved a mean absolute error (MAE) of 0.002424, a root mean square error (RMSE) of 0.003984, and a mean absolute percentage error (MAPE) of 0.123050, with a coefficient of determination R2 of 0.999840. These results indicate that the ECOA-BiTCN-BiLSTM model outperforms the other prediction models across all evaluated metrics, offering higher prediction accuracy and highly effective prediction models. |
| format | Article |
| id | doaj-art-05b8ccf331db450ea204d7d411131319 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-05b8ccf331db450ea204d7d4111313192025-02-09T12:29:58ZengNature PortfolioScientific Reports2045-23222025-02-0115111410.1038/s41598-024-74097-xPrediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid modelYi Zhang0Pengtao Liu1Yingying Xu2Meng Zhang3College of Electrical and Computer Science, Jilin Jianzhu UniversityCollege of Electrical and Computer Science, Jilin Jianzhu UniversityCollege of Electrical and Computer Science, Jilin Jianzhu UniversityKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin UniversityAbstract This paper presents a hybrid prediction model, ECOA-BiTCN-BiLSTM, for predicting dew in cold areas. The model integrates BiTCN and BiLSTM neural networks to enhance performance. An enhanced Crayfish optimization algorithm (ECOA) with four mixed strategies was employed to optimize the model’s hyperparameters and reduce the impact of arbitrary selection. The proposed ECOA-BiTCN-BiLSTM model was validated using dew data from farmland in a northeastern Chinese city. Comparative experiments were conducted against the BiTCN model, the BiLSTM model, the original BiTCN-BiLSTM model, and other models optimized with advanced swarm intelligence algorithms. The experimental results demonstrate that the proposed model achieved a mean absolute error (MAE) of 0.002424, a root mean square error (RMSE) of 0.003984, and a mean absolute percentage error (MAPE) of 0.123050, with a coefficient of determination R2 of 0.999840. These results indicate that the ECOA-BiTCN-BiLSTM model outperforms the other prediction models across all evaluated metrics, offering higher prediction accuracy and highly effective prediction models.https://doi.org/10.1038/s41598-024-74097-xSwarm intelligence optimization algorithmEnhanced crayfish optimization algorithmBiTCN-BiLSTM modelHyperparameter optimizationDew volume prediction |
| spellingShingle | Yi Zhang Pengtao Liu Yingying Xu Meng Zhang Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model Scientific Reports Swarm intelligence optimization algorithm Enhanced crayfish optimization algorithm BiTCN-BiLSTM model Hyperparameter optimization Dew volume prediction |
| title | Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model |
| title_full | Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model |
| title_fullStr | Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model |
| title_full_unstemmed | Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model |
| title_short | Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model |
| title_sort | prediction of cold region dew volume based on an ecoa bitcn bilstm hybrid model |
| topic | Swarm intelligence optimization algorithm Enhanced crayfish optimization algorithm BiTCN-BiLSTM model Hyperparameter optimization Dew volume prediction |
| url | https://doi.org/10.1038/s41598-024-74097-x |
| work_keys_str_mv | AT yizhang predictionofcoldregiondewvolumebasedonanecoabitcnbilstmhybridmodel AT pengtaoliu predictionofcoldregiondewvolumebasedonanecoabitcnbilstmhybridmodel AT yingyingxu predictionofcoldregiondewvolumebasedonanecoabitcnbilstmhybridmodel AT mengzhang predictionofcoldregiondewvolumebasedonanecoabitcnbilstmhybridmodel |