IoT-based machine learning comparative models of water quality prediction for freshwater lobster - Bakhit, Abdelmoneim Ahmed; Nur Syahirah, Mohd Sabli; Mohd Faizal, Jamlos; Mohd Aminudin, Jamlos; Nurhidayah Hidayah, Ramli; Muhammad Aqil, Hafizzan Nordin; Nura A., Alhaj; Ali, Ehtesham (Penerbit Akademia Baru, 2024)
Water quality parameters such as dissolved oxygen, potential hydrogen, and mineral content are important factors for aquaculture. Predictive analytics can predict water conditions in aquaculture and significantly reduce the mortality probability of aquaculture products. This paper applied predictive analytics to the freshwater lobster farming dataset. Three models, Autoregressive Integrated Moving Average (ARIMA), Neural Network Autoregressive (NNETAR), and Naïve Bayes, were run and evaluated in R Studio. These three models evaluated the performance of freshwater lobster water conditions, dissolved oxygen (DO), potential hydrogen (pH), electrical conductivity (EC), and total dissolved solids (TDS). The data was collected for six months, and 70% was used as training data and 30% as test data. Compared to NNETAR and Naïve Bayes, ARIMA fits the entire data set well for 7 days; the ARIMA model exhibited lower absolute errors for pH and electrical conductivity, with errors ranging from 0.04 to 1.7 across days, while the NNETAR model had generally lower errors for TDS, with errors ranging from 0.3 to 0.7; however, the Naïve Bayes model's performance varied, with the lowest error for DO on day (5) 0.15 but higher errors for other parameters and days, including the highest error for electrical conductivity on day (6) 6.2. In conclusion, the average absolute errors for DO, pH, EC, and TDS are 0.163, 0.064, 0.705, and 0.498, respectively.