Optimizing prediction of key water quality parameters in tilapia river based cage culture system using simple parameters based on different deep learning models - Jongjaraunsuk, Roongparit; Taparhudee, Wara (Agriculture and Natural Resources, 2025)
Importance of the work: Deep learning (DL) models can predict key water quality values in red tilapia culture using easily measured parameters. Objectives: To enhance the efficiency and effectiveness of water quality monitoring by developing a predictive DL model. Materials and Methods: Convolutional neural network (CNN), long short‒term memory (LSTM) and a hybrid CNN‒LSTM model were fine‒tuned using the Python software. Model performance was assessed using root mean square error (RMSE), mean absolute error (MAE), normalized root mean square error (NRMSE), Nash‒Sutcliffe efficiency (NSE) and the coefficient of determination (R²). The results were statistically analyzed based on a t test. Results: During data collection, the mean ± SD values of water quality parameters were: dissolved oxygen (DO), 4.03 ± 0.41 mg/L; water temperature (Temp), 27.63 ± 1.42°C; water pH level (pH), 7.45 ± 0.11; total ammonia nitrogen (TAN) at 0.14 ± 0.04 mg/L; nitrite‒nitrogen (NO2-‒N), 0.04 ± 0.05 mg/L; alkalinity (ALK), 105.41 ± 9.94 mg/L; and water transparency (Trans), 75.31 ± 22.80 cm. The study evaluated the CNN, LSTM and CNN‒LSTM models, with CNN‒LSTM consistently offering the best balance of accuracy and processing speed. Specifically, it excelled at 1,000 epochs for DO and TAN predictions and at 2,000 epochs for NO2-‒N and ALK predictions, with no significant differences compared to observed values using standard measurement methods. Main finding: The hybrid CNN‒LSTM model that used easily measurable water quality parameters (Temp, pH and Trans), effectively predicted more difficult‒to‒measure water quality parameters (DO, TAN, NO2-‒N and ALK). Additionally, the hybrid model outperformed the individual CNN and LSTM models, providing better prediction accuracy and faster processing times.