Collaborative Forecasting and Analysis of Fish Catch in Hokkaido From Multiple Scales by Using Neural Network and ARIMA Model

Collaborative Forecasting and Analysis of Fish Catch in Hokkaido From Multiple Scales by Using Neural Network and ARIMA Model

Fishery catch forecasting is a crucial aspect of aquatic research because of its relevance to establishing effective fishery management and resource allocation systems. In this study, we aim to forecast and analyze fish catch by collaboratively processing data using methods at multiple scales. To th...

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Bibliographic Details
Main Authors: Yue Zhang, Masato Yamamoto, Genki Suzuki, Hiroyuki Shioya
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
ARIMA model
fishery catch forecasting
neural network
Online Access:https://ieeexplore.ieee.org/document/9676650/

Internet

https://ieeexplore.ieee.org/document/9676650/

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