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Smart Dashboard for Water Quality Monitoring and Predicting in Koi Fish Farming Using IoT and Sarima Modeling | IEEE Conference Publication | IEEE Xplore

Smart Dashboard for Water Quality Monitoring and Predicting in Koi Fish Farming Using IoT and Sarima Modeling


Abstract:

Maintaining optimal water quality is critical for ensuring healthy growth and minimizing mortality rates in koi fish farming. This study develops a smart dashboard system...Show More

Abstract:

Maintaining optimal water quality is critical for ensuring healthy growth and minimizing mortality rates in koi fish farming. This study develops a smart dashboard system using IoT sensor data to monitor and predict key water quality parameters, specifically pH levels, Total Dissolved Solids (TDS), and temperature. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is utilized to analyze data collected from April 1st to April 30th, 2024, capturing seasonal variations. Augmented Dickey-Fuller (ADF) tests confirm the data's stationarity, validating the accuracy of the models. SARIMA(1,0,1)(1,0,1)[S] is selected as optimal due to minimal seasonal influence. The dataset is allocated into 80% for training and 20% for testing sets, demonstrating strong predictive accuracy for pH (MAPE = 5.08%) and TDS (MAPE = 2.86%), while highlighting areas for improvement in temperature predictions (Temperature In MAPE = 19.38%, Temperature Out MAPE = 22.44%). The developed smart dashboard facilitates real-time monitoring, thereby enhancing operational efficiency and promoting sustainable aquaculture practices.
Date of Conference: 07-08 August 2024
Date Added to IEEE Xplore: 08 October 2024
ISBN Information:
Conference Location: Bandung, Indonesia

I. Introduction

Water quality is a crucial factor in koi fish farming [1]. Unexpected weather and environmental changes pose challenges in maintaining pond water quality [2]. Fluctuations in pH levels, temperature, and turbidity can affect fish health. Accurate forecasting is required to predict future changes in the water quality of koi ponds [3].

References

References is not available for this document.