An intelligent framework for prediction and forecasting of dissolved oxygen level and biofloc amount in a shrimp culture system using machine learning techniques

作者：

Highlights：

• Predictive model for average dissolved oxygen (DO) levels is robust and reliable.

• Maintaining optimal flocculation level in pond helps in healthy growth of shrimp.

• Feature selection via Mutual Information is highly accurate for DO prediction.

• Including Meteorological parameters in model development improves model accuracy.

摘要

•Predictive model for average dissolved oxygen (DO) levels is robust and reliable.•Maintaining optimal flocculation level in pond helps in healthy growth of shrimp.•Feature selection via Mutual Information is highly accurate for DO prediction.•Including Meteorological parameters in model development improves model accuracy.

论文关键词：Prediction testing,Brackish water aquaculture system,Biofloc system,Machine learning methods,Dissolved oxygen and biofloc amount

论文评审过程：Received 27 August 2021, Revised 22 March 2022, Accepted 30 March 2022, Available online 4 April 2022, Version of Record 7 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117160