Main Article Content
Rearing Betta splendens is one of the most popular aquarium hobbies around the world. There are many IoT solutions done so far to monitor the water quality of the aquarium. Some machine learning and IoT based solutions are also available to do regression on the sensor data. In this paper, we propose a new framework and algorithm to predict the abnormalities in water quality which may affect the health of the fish. The algorithm proposed in this paper uses a local maxima niching genetic algorithm for optimization which effectively finds the local maxima on the new data streaming in and provides the approximate timestamp on the next possible water change or treatment to avoid the fish from getting infected. Many existing timestamp methods are seasonal but in terms of optimization in terms of unpredictable environment such as water, there needs a better technique for optimization. The qualitative and quantitative results proved that the health of fish using the proposed framework had better living conditions and avoided the attack of parasitic infections than those in existing and normal captivity methods. The accuracy of the proposed methodology increased by 5% within the variations made.
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