Monitoring and Forecasting Water Environment Parameters for Smart Aquaculture Using LSTM

The goal of the paper is to develop a system capable of automatically updating the parameters of the water environment periodically on the website and providing forecasts of water parameters for users. The system is simple to use and can be applied in many places. It has been partially built, including measuring parameters (pH, temperature, salinity, dissolved oxy-gen concentration (DO)), displaying, storing locally, and sending data to the website. This paper focuses on the analysis and design of sensor signal processing circuits and builds an algorithm to predict water parameters using long short-term memory (LSTM) model. The experiment results show that the circuit has worked to meet the requirements and the environmental parameters have been measured with 90% accuracy. This result shows that the system is feasible when applied in practice.