IoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks

The Internet of Things (IoT)-based smart solutions have been developed to predict water quality and they are becoming an increasingly important means of providing efficient solutions through communication technologies. IoT systems are used for enabling connection between various devices based on the...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 1925 - 21
Hauptverfasser:	Sundararajan, Suma Christal Mary, Shankar, Yamini Bhavani, Selvam, Sinthia Panneer, Manogaran, Nalini, Seerangan, Koteeswaran, Natesan, Deepa, Selvarajan, Shitharth
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 14.01.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 639/705 Animals Aquaculture - methods Aquaponic fish ponds Aquaponics Autoencoder Automation Convolutional Neural Networks Environmental Monitoring - methods Fish ponds Fishes Humanities and Social Sciences Internet of Things Multi-scale feature fusion-based convolutional autoencoder with gated recurrent unit multidisciplinary Ponds Prediction models Revamped fitness-based mother optimization algorithm Science Science (multidisciplinary) Water monitoring Water quality Water Quality - standards Water quality prediction Internet of things Aquaponic fish ponds Water quality prediction Multi-scale feature fusion-based convolutional autoencoder with gated recurrent unit Revamped fitness-based mother optimization algorithm
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!