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

Christan Hail Mendigoria, Ronnie Concepcion, Argel Bandala, Elmer Dadios, Oliver John Alajas, Heinrick Aquino, Ryan Rhay Vicerra, Joel Cuello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationTENCON 2021 - 2021 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665495325
StatePublished - 2021
Event2021 IEEE Region 10 Conference, TENCON 2021 - Auckland, New Zealand
Duration: Dec 7 2021Dec 10 2021

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2021 IEEE Region 10 Conference, TENCON 2021
Country/TerritoryNew Zealand


  • aquaculture
  • genetic algorithm
  • machine learning
  • neural network
  • nitrate concentration
  • physico-limnological

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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