Optimization of TCN-BiLSTM for dissolved oxygen prediction based on improved sparrow search algorithm

Dissolved oxygen (DO) is a crucial indicator of water quality in river ecosystems, and its accurate prediction plays a vital role in the protection and sustainable utilization of these ecosystems. However, current DO prediction models often struggle with issues such as noise in the water quality dat...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 30790 - 18
Hauptverfasser:	Shi, Pei, Tang, Mingjie, Wang, Quan, Ma, Xiaofei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 21.08.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/705/1042 639/705/117 Accuracy Algorithms Aquatic ecosystems Artificial intelligence Combinatorial models Datasets Deep learning Dissolved oxygen Dissolved oxygen prediction Feature selection Genetic algorithms Humanities and Social Sciences Long short-term memory Machine learning multidisciplinary Neural networks Optimization algorithms Oxygen Prediction models Research methodology Science Science (multidisciplinary) Sparrow optimization algorithm Temporal convolutional networks Time series Trends Water quality Wavelet transforms Sparrow optimization algorithm Combinatorial models Temporal convolutional networks Dissolved oxygen prediction
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!