A model based on an artificial neural network for assessing water quality on large shrimp farms.

José Juan Carbajal Hernández, Luis P.Sánchez Fernández, Ignacio Hernández Bautista, Jorge Hernández López

Research output: Contribution to journalArticle


Aquaculture is a commonly practiced activity worldwide. In Mexico, shrimp represents a significant source of the income generated by aquaculture. Since the success of shrimp farming depends on good water quality, its monitoring is essential. This work presents a new computational model to assess the water quality of large shrimp ponds (Litopenaeus vannamei). An artificial neural network (ANN) was used to create a water quality index, with which a mathematical relationship can be established between the dynamics of environmental parameters and different water quality conditions (excellent, good, average, and poor). Four parameters that were important for the habitat were selected: Temperature, dissolved oxygen, salinity, and pH. The results show that the proposed model performs well and efficiently, as compared to other evaluation models used for this purpose. The evaluations demonstrate that ANN is a good option for evaluating and detecting optimal and undesirable conditions, contributing to good water management for this type of farming.
Original languageAmerican English
Pages (from-to)71-89
Number of pages19
JournalTecnologia y Ciencias del Agua
StatePublished - 1 Sep 2017


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