Research on deep learning-based water quality prediction and management for intelligent aquaculture systems

Ji-Yeon Kim, Ki-Hwan Kim, Young-Jin Kang, Seok Chan Jeong

Keywords: Water Quality Prediction, Aquaculture, Fish

TL;DR: This study compares LSTM and linear regression models for predicting nitrogen compound levels in aquaculture water, showing that time-series deep learning significantly improves prediction accuracy and enables intelligent water quality management.

Abstract: This study develops a deep learning model for predicting nitrogen compound concentrations in aquaculture systems using time-series data. The LSTM-based approach significantly improves water quality prediction accuracy and enables intelligent management for sustainable fish farming.

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Submission Number: 24