Assessment and prediction of the water quality in shrimp culture using signal processing techniques

In recent years, artificial intelligence methods have demonstrated to be appropriated for treatment of environmental problems. This paper presents a novel work for assessment and prediction of the water quality in shrimp aquaculture based on environmental pattern processing. Water quality studies are based on analyzing negative concentrations of compounds in shrimp ponds that inhibit the good growing and reproduction of organism. The physical-chemical variables are classified basing on the negative ecological impact using the Gamma (Γ) classifier, which calculates the frequency and the deviation of the measurements from a specific level. A fuzzy inference system processes the level classifications using a reasoning process that determines when a specific concentration is good or harmful for the organism, and providing a water quality index, which describe the condition of the ecosystem: excellent, good, regular and poor. An autoregressive model (AR) predicts a section of an environmental signal using historical information, the set of predicted variables are assessed in order to estimate future water quality conditions in the system. This methodology emerges as a suitable and alternative tool to be used in the developing effective water management plans.