Diagnóstico y predicción del hábitat en la camaronicultura
Assessment and Prediction of Water Quality in Shrimp Culture
José Juan Carbajal Hernández y Luis Pastor Sánchez Fernández
Centro de Investigación en Computación, Instituto Politécnico Nacional, México. jcarbajalh@cic.ipn.mx, lsanchez@cic.ipn.mx
]]>Article received on 23/03/2012;
accepted on 02/03/2013.
Resumen
La calidad del agua puede ser evaluada directamente mediante pruebas de toxicidad y efectos negativos generados por los parámetros del agua, proporcionando un indicador de calidad del agua en estanques de cultivo de camarón. En este trabajo de tesis, se proponen tres modelos para el diagnóstico de la calidad del agua: Un modelo para el análisis instantáneo, un segundo para el análisis histórico y finalmente, un tercero para predecir la calidad del agua. Los parámetros de la calidad del agua son evaluados individualmente mediante un índice (gamma), determinando su impacto negativo y sus posibles consecuencias en el organismo. Mediante un sistema de inferencia difusa se evalúan aquellas situaciones negativas en el ecosistema, proporcionando dos índices de la calidad del agua: IWQI para diagnóstico instantáneo y HWQI para diagnóstico de un conjunto de mediciones. Asimismo, un modelo autorregresivo (AR) es empleado para predecir valores de los parámetros de calidad del agua, que a su vez, son evaluados por el sistema de inferencia difusa proporcionando un índice predictivo de calidad del agua (PWQI). Comparaciones de los modelos propuestos contra índices de la calidad del agua publicados en recientes investigaciones y por organismos internacionales, permiten resaltar la eficiencia y buen funcionamiento de nuestro modelo.
Palabras clave: Calidad del agua, sistema de inferencia difusa, procesamiento de señales, modelos autorregresivos.
Abstract
Diagnosis of water quality can be accessed through toxicity tests and by analyzing the negative effects of water quality parameters. Such diagnosis provides an indicator of the condition of the shrimp ecosystem. In this research, three models for water quality diagnosis were proposed: first, a model for immediate assessment, second, a model for historical evaluation and third, a model for prediction of water quality. We evaluated water quality parameters by their negative ecological impacts using a new index (gamma). A fuzzy inference system assessed each harmful situation presented in the pond, providing a final index of water quality: IWQI for immediate measurements and HWQI for a set of measurements. An autoregressive model (AR) was used for predicting values of each water quality parameter; these values were assessed by the proposed fuzzy inference system, establishing a predictive water quality index (PWQI). Comparisons of our models against classical models in the literature show good performance of the algorithms proposed in this work.
]]> Keywords: Water quality, fuzzy inference system, pattern processing, autoregressive models.
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Nota
Report on PhD thesis. Graduated: José Juan Carbajal Hernández, Advisor: Luis Pastor Sánchez Fernández. Graduation date: 28/07/2010.
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