Automated water quality monitoring and regression-based forecasting system for aquaculture

Abstract

Water quality in fish tanks is essential to reduce fish mortality. Many factors affect the water quality, such as pH, dissolved oxygen, and temperature in fish tanks. Existing work has presented water quality monitoring systems for aquaculture, which are useful for automatic monitoring and notify any incidence of decline in water quality. It enables the fish farms to make interventions to reduce fish mortality. However, advanced monitoring through forecasting is necessary to ensure consistent optimum water quality. This paper presents a web-based water quality monitoring and forecasting system for aquaculture. First, a water quality forecasting model based on the long short-term memory is designed and developed. The model is evaluated and fine-tuned using the existing public dataset. Second, the prototype of the water quality monitoring and forecasting system is developed. An Arduino and Raspberry Pi based water quality data acquisition tool is built. A web-based application is then developed to present the monitoring data and forecasting. A notification module is included to send an alert message to the fish farmers when necessary. The system is tested and evaluated at the fish hatchery in Universiti Malaysia Sabah. The findings show that the proposed system provides better water quality management for fish farms.