Development of a Low-Cost Solar Powered &amp; Real-Time Water Quality Monitoring System for Malaysia Seawater Aquaculture

Rostam, Nur Aqilah Paskhal; Malim, Nurul Hashimah Ahamed Hassain; Abdullah, Rosni

doi:10.1145/3416921.3416928

Cited by 4 publications

(10 citation statements)

References 16 publications

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“…Finally, a concept paper was proposed [52] on the Smart River Monitoring System for river sustainability. Inspired by these studies, additional modifications to the hardware to suit the low-cost concept for seawater monitoring system was studied and proposed in our previous work [3].…”

Section: B Monitoring and Profiling Water Quality For Growth Of Algaementioning

confidence: 99%

“…Harmful algal bloom (HAB) has long been a threat to water sources due to the rapid increase and accumulation of algae population that can cause harm. HAB toxin negatively affects human health, the environment, and the economy whilst non-toxic ones can damage fisheries resources and equipment [2], [3]. Harmful toxins such as neurotoxins released by the algae encourage decomposers' growth that increases the biochemical oxygen demand (BOD) of the water.…”

Section: Introductionmentioning

confidence: 99%

“…Our previous work [3] has managed to implement a solarpowered and low-cost water quality monitoring system (WQMS) for coastal studies. However, it was only a preliminary study focusing on simple data analysis of the parameter readings.…”

Section: Introductionmentioning

confidence: 99%

“…This paper extends from that previous work and will discuss in detail the modelling phase for the predictive modelling, especially in tackling the dynamic problem of algae ecological data and will briefly mention the enhancement progress of the previous system. Later, the chosen predictive modelling will be applied to the data collected in the previous study [3]. Hence, a complete framework is presented in this paper, which covers the end-toend process of developing a water monitoring system, deployment, installation, and prediction model development, which was missing in our previous work in this domain.…”

Section: Introductionmentioning

confidence: 99%

“…Coralline algae would attach to the equipment and damage all the installed sensors [3] which would later lead to missing data collection for the day. This is one of the main reasons why early algae prediction remains crucial as more time is provided for facilities that use coastal water to shut down before their equipment is damaged [22], [23].…”

Section: Introductionmentioning

confidence: 99%

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A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

et al. 2021

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An end-to-end process to achieve a complete framework methodology for Harmful Algal Bloom (HAB) growth prediction is crucial for water management, especially in implementing robust predictive modelling of HAB to prevent water pollution. Previous works have separately focused on the prediction part or the implementation of the water monitoring system that involves the integration of sensors through the Internet of Things (IoT). These studies lack in terms of discussion of both IoT with the algae ecological domain and prediction method. Therefore, this paper takes the initiative to provide a wider coverage on the end-to-end process including the assembly and integration of sensors, data acquisition and predictive modelling using data-driven approaches, for example, machine learning, deep learning and deep time series forecasting algorithm for future algal bloom outbreak mitigation. This paper believes that discussion in a complete framework perspective based on the execution of each phase is important besides providing a true understanding of the algae growth factors and prediction problems to achieve a robust prediction algorithm for algal growth. In the end, this paper presents proof that selecting the right features and utilising time series with deep learning are much better for tackling the issues of highly non-linear and dynamic algae ecological data that are briefly introduced in this paper. Among all the algorithms selected, Long Short-term Memory (LSTM) is the best fit for the prediction method and has outperformed other basic machine learning methods in accurately predicting algal growth through the prediction of chlorophyll-a (Chla) as a strong indicator of algal presence for coastal studies.

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Section: B Monitoring and Profiling Water Quality For Growth Of Algaementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

et al. 2021

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A Conceptual Software Framework to Monitor Harmful Algal Blooms (HABs) in Lakes

Sujatha,

Reza,

Ranganathan

2024

2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)

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

Wei

Tindik²,

Ching³

et al. 2023

Bulletin EEI

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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.

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Development of a Low-Cost Solar Powered & Real-Time Water Quality Monitoring System for Malaysia Seawater Aquaculture

Cited by 4 publications

References 16 publications

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

A Complete Proposed Framework for Coastal Water Quality Monitoring System With Algae Predictive Model

A Conceptual Software Framework to Monitor Harmful Algal Blooms (HABs) in Lakes

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

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