IWQMA: Intelligent Water Quality Management in Aquaculture using IoT Technology

Authors

  • Yamuna R Assistant Professor, Department of Computer Science and Engineering, Government Engineering College, Chamarajanagar, India
  • Harsharani K S Assistant Professor, Department of Computer Science and Engineering, Government Engineering College, Ramanagara, India
  • Manasa S M Assistant Professor, Department of Computer Science and Engineering, The Oxford College of Engineering, Bangalore, India
  • Sathya M Assistant Professor, Department of Computer Science and Engineering, The Oxford College of Engineering, Bangalore, India
  • Lenish Pramiee Assistant Professor, Department of Computer Science and Engineering, The Oxford College of Engineering, Bangalore, India
  • Asha A Kumari Assistant Professor, Department of Computer Science and Engineering, The Oxford College of Engineering, Bangalore, India

Keywords:

Internet of Things (IoT), Water quality, Aquaculture, Sensor nodes, Real-time data, Arduino processor, Control system, Cloud-based data transmission, GSM modem, Remote monitoring, Sustainability, Automation, Aquatic environment

Abstract

This work introduces an innovative approach to water quality management in aquaculture by harnessing the power of the Internet of Things (IoT) technology. Aquaculture, the controlled cultivation of aquatic species, necessitates stringent monitoring of water parameters to ensure the well-being and growth of aquatic life. In response to the critical need for real-time water quality assessment and control, we propose an Intelligent Water Quality Management in Aquaculture (IWQMA) system. The IWQMA system integrates a sensor module comprising Arduino-based sensors to gather real-time data on essential water quality parameters, including temperature, pH value, nitrate and ammonia composition, total suspended solids, and foul odor. This data is transmitted to a central processor, where it undergoes thorough analysis and preprocessing. Leveraging Random Forest algorithm, the system enhances predictive accuracy and provides valuable insights for proactive management. A key component of the IWQMA system is the use of the AutoRegressive Integrated Moving Average (ARIMA) model for time series forecasting. This model enables precise predictions of water quality parameters, supporting informed decision-making in aquaculture management. The comparative accuracy analysis of pH and ammonia concentration forecasts demonstrates the system's capability to provide accurate and reliable predictions, crucial for maintaining optimal water quality conditions in aquaculture. The IWQMA system not only offers a sophisticated solution for aquaculture but also holds the potential for broader applications in environmental monitoring and management. By advancing the fusion of IoT and machine learning, this research contributes to sustainable and ethical practices in aquaculture, fostering the well-being of aquatic species and the responsible utilization of technology. The IWQMA system represents a significant stride towards intelligent water quality management, offering a transformative framework for safeguarding the health and vitality of aquatic life in aquaculture while aligning with ethical research principles.

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Published

14-12-2023

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Articles

How to Cite

Yamuna R, Harsharani K S, Manasa S M, Sathya M, Pramiee, L., & Kumari, A. A. (2023). IWQMA: Intelligent Water Quality Management in Aquaculture using IoT Technology. TWIST, 18(4), 183-198. https://twistjournal.net/twist/article/view/66

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