In Situ Indirect Measurement of Nitrate Concentration in Outdoor Tilapia Fishpond Based on Physico-limnological Sensors

Excess nitrate concentration leads to excessive algal growth that reduces dissolved oxygen for aquatic animals. A significant strategy to preserve the water quality of aquatic systems is through nitrate level assessment. However, use of nitrate sensors and existing laboratory approach is costly and requires a huge effort. This study investigated the application of computational intelligence for measurement of nitrate concentration in a tilapia fishpond at Rizal province, Philippines, based on physico-limnological parameters such as temperature, electrical conductivity, and pH level. Artificial neural network (ANN) algorithms including feed-forward (FNN) and recurrent (RNN) neural networks were developed and optimized using genetic algorithm (GA) to improve their predicting performances. Genetic programming (GP), through GPTIPSv2 tool, was configured to generate a fitness function. This function is the principal component of GA optimization to produce optimal number of hidden neurons for ANN architecture that resulted in 2 neurons for GA-FNN and combination of 92, 31, and 11 neurons for each hidden layer using the GA-RNN model. Based on evaluation results, all models provided acceptable results with error and predictive accuracy values approaching 0 and 1, respectively. However, the GA-FNN model outperformed other models with 3.26 RMSE, 2.23 MAE, and 0.97 R
²
values which proved to be the most effective and suitable model for the indirect measurement of nitrate concentration.