Aquaculture 4.0: hybrid neural network multivariate water quality parameters forecasting model
Abstract This study examined the efficiency of hybrid deep neural network and multivariate water quality forecasting model in aquaculture ecosystem. Accurate forecasting of critical water quality parameters can allow for timely identification of possible problem areas and enable decision-makers to t...
Main Authors: | Elias Eze, Sam Kirby, John Attridge, Tahmina Ajmal |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-09-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-023-41602-7 |
Similar Items
-
Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
by: Elias Eze, et al.
Published: (2020-10-01) -
Gravitating toward supply chain 4.0
by: Nahida Sultana, et al.
Published: (2022-12-01) -
Manufacturing Companies Industry 4.0 maturity level: A multivariate analysis
by: Luis Miguel Ciravegna Martins da Fonseca, et al.
Published: (2024-02-01) -
From industry 4.0 to lab 4.0
by: Francesca Lake
Published: (2019-06-01) -
Machine learning-based optimal temperature management model for safety and quality control of perishable food supply chain
by: Joy Eze, et al.
Published: (2024-11-01)