Prediction of cold region dew volume based on an ECOA-BiTCN-BiLSTM hybrid model

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 hyperpa...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 4265 - 14
Hauptverfasser:	Zhang, Yi, Liu, Pengtao, Xu, Yingying, Zhang, Meng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 04.02.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/705 704/106 704/172 Accuracy Agricultural land Agricultural production Algorithms Artificial intelligence BiTCN-BiLSTM model Cold Cold regions Deep learning Dew Dew volume prediction Enhanced crayfish optimization algorithm Genetic algorithms Humanities and Social Sciences Humidity Hyperparameter optimization Machine learning multidisciplinary Neural networks Optimization algorithms Prediction models Science Science (multidisciplinary) Support vector machines Swarm intelligence optimization algorithm Hyperparameter optimization Dew volume prediction BiTCN-BiLSTM model Swarm intelligence optimization algorithm Enhanced crayfish optimization algorithm
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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