Elaboración de un modelo neuronal artificial para la estimación de la demanda bioquímica de oxígeno en aguas marinas

Contextualization: Artificial Neural Networks are models designed from numerical methods called Artificial Neural Networks. The use of these, as a Biochemical Oxygen Demand (BOD) prediction tool, has shown various advantages, among others, the reduction of time and the economic costs associated with this parameter. BOD usually requires 5 to 7 days, as well as multiple chemical reagents, to obtain the levels of organic materials in the waters.

Research gap: Artificial Neural Networks models allow calculating BOD in real time from physicochemical variables recorded in situ. Despite this, artificial neural networks have not been used until now as a method of estimating BOD in Colombian seawaters.

Purpose: Taking this aspect into account, an artificial neural network model that allows estimating the BOD in waters of the Colombian Caribbean Sea was developed in this research.

Methodology: For the elaboration of the model it was necessary to carry out five simulations (consisting of a number of 2 to 3 hidden layers, and 5 to 20 neurons per layer). The predictive performance of each of these simulations was evaluated through the correlation coefficient.

Results and conclusions: The highest values of this statistical indicator (0.937, 0.951, 0.953, and 0.941), were obtained for the model that used 3 layers, of 20 neurons each, in its four learning sets (training, validation, testing, and all data). These values indicate a close fit between the observed data and estimations made by the network. These results also demonstrate that Biochemical Oxygen Demand can be estimated numerically, in seawaters, through artificial neural networks models.

keywords: Levenberg-Marquardt, Caribbean Sea, neuron

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How to Cite

Meza Castellar, R., & Gonzalez Salcedo, L. (2020). Development of an artificial neural network model for estimation of bod in seawaters. Revista De Investigación Agraria Y Ambiental, 11(2), 147-156. https://doi.org/10.22490/21456453.3441

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