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Predictive Water Quality Management for Arowana Aquaculture Using Hybrid Iot And Fuzzy Time Series Models
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles
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Abstract
Arowana (Scleropages formosus) cultivation is a challenging endeavor, largely due to the species' sensitivity to water quality fluctuations. Traditional manual monitoring methods are often inefficient, prone to human error, and lack the foresight needed for proactive management. This article presents a novel approach to water quality management in arowana aquaculture by integrating real-time monitoring capabilities of the Internet of Things (IoT) with advanced predictive analytics using multivariate fuzzy time series (FTS) models. The proposed system continuously collects critical water parameters such as pH, dissolved oxygen (DO), temperature, turbidity, and conductivity. These real-time data streams are then fed into a sophisticated fuzzy time series model that forecasts future water conditions, enabling cultivators to anticipate and mitigate potential issues before they impact fish health. The implementation demonstrates the efficacy of a hybrid IoT-FTS framework in providing timely, data-driven insights for optimizing Arowana cultivation environments, contributing significantly to sustainable aquaculture practices and reducing economic losses associated with poor water quality. Through rigorous evaluation and validation, the proposed FTS-multivariate T2 model demonstrated superior performance, achieving an exceptionally low error rate, outperforming traditional regression models.
Keywords
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