Open Access

ARTICLE

Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

Po-Yuan Yang^1,*, Yu-Cheng Liao², Fu-I Chou²

1 Department of Intelligent Robotics, National Pingtung University, Pingtung, 900, Taiwan
2 Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 807, Taiwan

* Corresponding Author: Po-Yuan Yang. Email:

Computer Systems Science and Engineering 2023, 46(3), 2861-2880. https://doi.org/10.32604/csse.2023.036810

Received 12 October 2022; Accepted 06 January 2023; Issue published 03 April 2023

Abstract

Aquaculture has long been a critical economic sector in Taiwan. Since a key factor in aquaculture production efficiency is water quality, an effective means of monitoring the dissolved oxygen content (DOC) of aquaculture water is essential. This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality. Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality. Since aquaculture water quality depends on a continuous interaction among multiple factors, and the current state is correlated with the previous state, a model with time series is required. Therefore, this study used recurrent neural networks (RNNs) with sequential characteristics. Commonly used RNNs such as long short-term memory model and gated recurrent unit (GRU) model have a memory function that appropriately retains previous results for use in processing current results. To construct a suitable RNN model, this study used Taguchi method to optimize hyperparameters (including hidden layer neuron count, iteration count, batch size, learning rate, and dropout ratio). Additionally, optimization performance was also compared between 5-layer and 7-layer network architectures. The experimental results revealed that the 7-layer GRU was more suitable for the application considered in this study. The values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%, root mean square error of 0.0638, and R-value of 0.9984. Therefore, the water quality management system developed in this study can quickly provide practitioners with highly accurate data, which is essential for a timely response to water quality issues. This study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery company. Practical application of the system by the fishery company confirmed that the monitoring system is effective in improving the survival rate of farmed fish by providing data needed to maintain DOC higher than the standard value.

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