TY - JOUR
T1 - Modelo basado en redes neuronales artificiales para la evaluación de la calidad del agua en sistemas de cultivo extensivo de camarón
AU - Hernández, José Juan Carbajal
AU - Fernández, Luis P.Sánchez
AU - Bautista, Ignacio Hernández
AU - López, Jorge Hernández
PY - 2017/9/1
Y1 - 2017/9/1
N2 - 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.
AB - 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.
KW - Water quality
KW - aquaculture
KW - artificial neural networks
KW - shrimp.
UR - http://www.scopus.com/inward/record.url?scp=85031704499&partnerID=8YFLogxK
U2 - 10.24850/j-tyca-2017-05-05
DO - 10.24850/j-tyca-2017-05-05
M3 - Artículo
SN - 0187-8336
VL - 8
SP - 71
EP - 89
JO - Tecnologia y Ciencias del Agua
JF - Tecnologia y Ciencias del Agua
IS - 5
ER -