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

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Mendigoria:2021:TENCON,

• author = "Christan Hail Mendigoria and Ronnie Concepcion and Argel Bandala and Elmer Dadios and Oliver John Alajas and Heinrick Aquino and Ryan Rhay Vicerra and Joel Cuello",
• booktitle = "TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)",
• title = "In Situ Indirect Measurement of Nitrate Concentration in Outdoor Tilapia Fishpond Based on Physico-limnological Sensors",
• year = "2021",
• pages = "498--503",
• abstract = "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 R2 values which proved to be the most effective and suitable model for the indirect measurement of nitrate concentration.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/TENCON54134.2021.9707207",
• ISSN = "2159-3450",
• month = dec,
• notes = "Also known as \cite{9707207}",

}

Genetic Programming entries for Christan Hail Mendigoria Ronnie S Concepcion II Argel A Bandala Elmer Jose P Dadios Oliver John Y Alajas Heinrick L Aquino Ryan Rhay P Vicerra Joel Cuello

Citations